If genre, then, is an institution, it is logical that in it, in addition to the founder who draws a first model or programmatic work, there will be affiliates that follow to the letter and scrupulously the founder as model, idlers who forget it; reformers who renew it with vigor or adapt it to new historical circumstances; detractors who criticize, contradict or parody it, seeking their limitations; theoreticians who at every moment try to fix, sometimes pedantically, its characteristics; annihilators who fight and finish it, destroying or exhausting it; continuers who pick up the prestige of its name for new realities founded by them, or who in different periods will put new names to things that in the end are so similar that they could be called with the same denomination. (Díez Taboada 1965, 15, my translation).

The use of institutions as a model to understand genres has been used since then in different traditions. Todorov (1976) or Voßkamp (1977) use it mainly to explain the communicative function of genres between the authors and their contemporary readers. Through the label, the author communicates the category of the text and therefore awakens a series of expectations in the reader based on other texts. In many cases, the author actually wants to modify this *horizon of expectations*, a concept coined by Jauß (1970). With this process, labels change over time, becoming associated with other expectations.The historical reconstruction of this communicative process is the central goal of many of these articles which have a longitudinal perspective, which will be addressed in Section <IP_ADDRESS>.

In any case, the social institution metaphor is not tied to this historical reconstruction, nor to the communication between author and readers. This metaphor can also be understood by recognizing that texts participate in genres, as people participate in institutions. The participation is not necessarily defined by the writer, it can also be assigned by other agents, even decades or centuries later. In addition, these institutions do not necessarily have to be observed in their historical context, but may be contrasted against each other.

In essence, the family resemblance and social institution model dispose of the requirement for finding necessary and sufficient conditions and instead propose a loose relationship between their referents (instances) and the concepts (or features in computational terms).

#### <IP_ADDRESS> Prototype Model

In addition to family resemblance, the other competing model for genres often mentioned in the research is the prototype theory. This model dates back to 1973, when Rosch analyzed the learning process of a group of teenage speakers of the Indonesian language Dani. This group was chosen because its language does not have lexicalized terms for color or basic forms. She conducted two experiments in which the participants were required to learn the names of colors and specific forms, measuring the ratio of error for each element. The results found that central items (e.g. a perfect square) were learned more easily than modifications (e.g. a freehand square or a square with a gap on one side).

The terms *central, focal* or *prototypical* are used synonymously throughout the article. In her conclusion, Rosch proposes that more complex categories (birds or fruits) could have similar cognitive models (349).These were included in the ten categories that she analyzed in a follow-up article (1975) gathering

answers from university students. The respondents tended to agree when giving scores for items that were seen as better examples of the different categories: Apples and oranges are more clearly identified as fruits than coconuts or pawpaws; robins or sparrows are more often selected as good examples of birds than emus or penguins. This last category was visualized by Aitchison (2012, 69, originally published in 1987) and has been widely disseminated (even translated, for example into Spanish in Escandell Vidal 2012, 174).

*Figure 4: Birdiness rankings by Aitchison (2012, 69)*

Although the illustration of Figure 4 is very efficient in showing that the robin is *birdier* than the penguin, there is an important flaw: The vertical and horizontal axis are not labeled. The peacock and the pheasant appear to be at different poles of the vertical axis, and a similar effect is observed for the toucan and the parrot on the horizontal axis. This leaves the reader wondering what this spatial distribution represents. The reason for the lack of labels is very simple: The two dimensions do not actually mean anything, thus the reader is misled. As with the squares in Rosch's paper of 1973 shown in Figure 3, the birds should have been arranged along an axis (horizontal or vertical), with the robin as the positive extreme (the best exemplar) and the penguin and ostrich at the negative pole (the worst ones). The word *central* appears in Rosch's 1973 paper up to 58 times, many times as a synonym for *focal* or *prototypical*, but was dropped in her paper of 1975. The concept of central has its purpose in the example of the colors, where the wavelength creates a natural arrangement, but not in the other categories.

In summary, in Rosch's prototype model, each item does not belong to the concept in categorical terms (i.e. it does or it does not), but rather in terms of degree (i.e. it belongs clearly to the category or it belongs vaguely to the category).

One of the most comprehensive papers on the application of the family resemblance and prototype theories to genres was written by Hempfer (2014)3 who initially rejected taxonomies as an accurate model for genres (405–6) and the nominalistic critique against genre as a tautology (409). Hempfer proposed the model of family resemblance to explain the instances (texts) where there is no agreement on a basic set of features in order to belong to a genre (409). To operationalize the ahistorical invariants (archi-texts or modes in Genette), he proposed the model of the prototype (414), remarking that:

[...] the fundamental difference between the 'classical' concept of class as a means for categorization and the prototype concept therefore consists in the fact that a class tolerates only membership or non-membership, while membership in a prototype category is 'a matter of gradience.' (Hempfer 2014, 411).

In my opinion, the prototype and family resemblance theories are competing models to the classic scholastic-structuralist theory, but they are not mutually exclusive. Both modify different sections of the semiotic triangle:

3 Hempfer had written before a monograph about genres (1973). Although he cites Wittgenstein, he does not refer to the family resemblance as model for genres. Of course, he also does not mention the prototype model since Rosch was publishing her first articles on the topic that same year.

Both aspects can be combined to explain the distinct particularities of the genres, as discussed in Chapters 7.1 and 8.

## **2.3.3 Macro-Models of Genres**

The four models presented above (classic scholastic-structuralist, hybridism, family resemblance, and prototypes) define the way that the referents must display a series of conditions defined by the concept in order to be labeled within a specific category. Nevertheless, these models have not established how the different categories relate to each other. Thus, in this section, I am not examining the referents, but only how a category (e.g. adventure novel) relates to other categories (e.g. historical, war novel, etc.). One can intuitively accept that an adventure novel is a type of novel, and that adventure novels and historical novels have more similarities between them than with romantic novels. It could also be argued, that specific subgenres constitute a macrosubgenre (for example, historical and adventure novels). These relationships between genres and subgenres are what I call the *macro-models of genre*, 4 and in this section I will present some of the most common ones.

#### <IP_ADDRESS> Taxonomy

By far the most frequent macro-model is the taxonomy, which imposes a strict composition in which the concepts (e.g. genres) can be classified in tree-like

4 In previous publications, I have used the concept structure of genres. I now prefer to use the concept macro-model for two reasons. The first is that by using the prefix macro, it is clear that larger components of the model are being treated. The second is that I will use the term structure in Chapter 8 to refer to the formalization of the model, following Weisberg's terminology (2013). Thus, the different possibilities presented in the previous Section 2.3.2 could have been called micro-models of genre. However, it is unusual to find the prototype model presented as a micro-model.

structures. Actually, this macro-model is a consequence of the concepts of *genus proximum* and *differentia specifica* of the scholastic model seen in Section <IP_ADDRESS>: Every node (e.g. *historical novel*) hangs from an upper branch (its *genus proximum*, or in semantic terms, its hypernym, in this case *novel*) and has specific features (its *differentia specifica*) that distinguish it from the other sibling nodes (Fricke 2010b, 8). For example, a historical novel can be a novel in a historical setting with the intention of representing the reality of that time. This macro-model is more or less explicit in many essays, often applied without real examples (Garrido Gallardo 1988, 26). Figure 5 shows the epic-narrative taxonomy as presented by García Berrio and Huerta Calvo (1992, 171).

*Figure 5: Taxonomy of narrative-epic genres by García Berrio and Huerta Calvo (1992, 171)*

Taxonomy, as a macro-model of the conceptual understanding of the genres similar to the species in biology, is surprisingly frequent, even in our time (Schaeffer 2006, 9). This conception was taken further by authors like Brunetière, who, in 1880, proposed a Darwinian evolution of the genres, in which they would compete against each other (see Petersen 1944, 121 or Schaeffer 2006, 33 for a critique).The idea that genres can be explained with animal similes is also present in Moretti's hypothesis that states that genres tend to survive over a similar span of years as living beings. In his hypothesis, genre would be phenomena that tend to live around 25 years, dying with a "total

change of their ecosystem" (2005, 20). This hypothesis has been recently analyzed and rejected by Underwood (2016, 2019).

Genette (1988) proposes a distinction between archi-genres (also called modes, like lyric, epic, and dramatic, but potentially also others) that would contain specific genres, creating taxonomies; the examples that Genette gives are: *épique > novel > roman policier > roman policier "réaliste"*. Although Genette criticizes this, he also followed the three-part schema in his article and defined the source of the speech act as the most fundamental criterion: delivered by the poet, by the characters, or a combination of both (1988, 206).

Vivas (1968) analyzed the reasons why artists and theorists see the concept of genre as useless. He accepted that by the beginning of the 20th century, many artists turned "their backs on the perfected form" of the novel and tried new forms (97). Together with this, other sociological and artistic processes would have affected the role of genre, such as easier access to culture, the influence of idealistic aesthetics, new aesthetic needs of the public to access the artistic object without previous labeling, and the needs of the readers (98–100). He criticized taxonomy as a structure for genres, saying that it "is the absurd extreme to which the notions of classes and genres have been carried sometimes by men suffering from a taxonomic itch" (99–100). These taxonomies are the product of the scholastic model of concepts applied to genre, and therefore to point out their invalidity is to implicitly negate that genres can be explained as *genus proximum* plus *differentia specifica*.

#### <IP_ADDRESS> Flat Macro-Model

As I have described in Chapter 2.2, the multi-genre assignment is the most typical perspective for computational studies, normally treated as a multilabel classification task: Any text is assigned to one or more labels and the algorithm is trained to classify each text as either being part of a group or not being part of it. This multi-label macro-model is able to fit the fact that normally the sources use several categories for each text rather than a single category. Nevertheless, it is indisputable that some genres are more similar than others: Memoirs, autobiographies, and educational novels are somehow closer to each other than to erotic or adventure novels. This similarity between genres (that can be visualized to a certain degree in the taxonomy) is completely ignored in the traditional multi-label classification task from computational research. For this reason, I consider the multi-label classification task to use a flat macro-model of genres in which any text belongs to one or

more genres in a categorical way, without describing whether the genres are very close or very distinct.

#### <IP_ADDRESS> Gradual Macro-Model

In addition to taxonomy, and the flat macro-model, there is a further type of relationship between genres that has been used at different moments in the story, i.e. in terms of degree. Julius Petersen (1944, originally published in 1939) created a theoretical macro-model for literary genres in which every historical genre (lyrical novel, hymn, story, fable in his examples) had a position in a three-dimensional macro-model.The three dimensions were Goethe's universal modes (dramatic, epic and lyric, see Section <IP_ADDRESS>), and every historical genre was related in terms of degree to two of these modes. He called this macro-model the *compass of the genres*.

In this macro-model, the three modes (dramatic, epic, and lyric, called *Grundformen*, English: 'basic forms') have different levels of degree, with the *Urdichtung* (translatable as 'proto-poetry*'*) at the center of the macro-model (124–25). These three basic modes are combinable pairwise, creating the historical genres (*Zwischengattungen*, 'between-genres'):

Between poetry and epic are those species whose form is more or less characterized as a monologic report of a state: elegy, epistle, vision, idyll, and lyrical novel. Between epic and drama moves the dialogic report of an action: frame narrative, letter novel, dialogue novel. Between poetry and drama stands the dialogic representation of states: lyrical conversation, heroic, cantata, dramatic idyll, lyrical drama. (Petersen 1944, 123–24, my translation).

In this macro-model, every historical genre is defined not as a subtype of an archi-genre (as in a dendrogram in which hybridism does not fit), but as a combination of two modes or basic forms. This combination is not represented categorically, but rather in degrees, where the closeness of one to another represents the degree of participation. For example, the lyric drama is both dramatic and lyrical and that is why it sits between both modes; but since it is more dramatic than lyrical, it is closer to drama.

The accuracy, utility, or transferability to other languages of this *compass of the genres* has been criticized (for example by Genette 1988), and, in my opinion, its limitations are shared by many other models presented here: precision of the exact values of each genre. The macro-model could be improved by treating it as a multi-dimensional space in which each genre can be de-

*Figure 6: Compass of the genres, by Petersen (1944, 125)*

scribed not only in two, but in three, or even more literary modes. Nevertheless, I think this macro-model has many important novelties. First, unlike the flat macro-model of the multi-label classification task, it asserts that some historical genres are closer to others. Second, it accepts hybridism as a core characteristic of historical genres and not an exception, thus moving away from the taxonomic macro-model. Third, it foresees that each genre can be described in terms of degree in a bi-dimensional space. Finally, it places many heterogeneous genres in a single abstract macro-model. The disadvantage of this macro-model is that neither instances (texts) nor the features of these dimensions are represented or clear.

### **2.3.4 Perspectives on Genres**

A further aspect in the research on genres is how the analyzed corpus is defined. Two main perspectives on the studies can be differentiated: longitudinal historical and cross-sectional synchronic. In the following section, I will further describe these two perspectives, giving examples for each of them from Literary Studies, Linguistics, and Digital Humanities.

#### <IP_ADDRESS> Longitudinal Historical Studies

In longitudinal historical studies, the researcher is interested in reconstructing the historical evolution of specific genres or subgenres. These cases normally cover longer periods (over several decades or centuries), using a handful of categories or fewer as an example. One important aspect is what the author's intention was when using specific labels and what were the expectations of the readers at the time. These highly qualitative labels are time-consuming to collect, and there is little clarity on the exact type of source they should come from: The cover can be interpreted as a rather superficial source that is influenced by the publisher. Besides, for many periods, the number of books that identified a genre on the cover or in the para-text is often small, and therefore the researcher only analyzes a small fraction of the literature of that period.

This longitudinal historical perspective does not relate to any area of research: Both Literary Studies, Linguistics, or Digital Humanities can include the recompilation of data and analysis in this manner, as described below in examples from the different disciplines.

For example, the historical development of genres is a key aspect in an influential work in the German tradition of literary genres by Raible (1980). He summarized five characteristics of literary genres from a semiotic perspective. For him, literary genres are conceptual models that try to reduce the complexity of the texts that are grouped together (322). He described five consequence. First, genres are historical conventions with a normative function. Second, genres show different levels of complexity (e.g. novels can contain short novels, therefore novels are more complex than short novels). Third, in genre, the reader expects specific phenomena in a given order. Fourth, the reader uses the information about the genre to interpret the text (e.g. laugh about or mourn a death depending on whether the text is a tragedy or a parody, 334). Fifth, genres are defined by external text features, such as the goals

of the author, the author's situation, and the situations of the readers. Finally, he proposed a methodology for describing literary genres in six dimensions, each one containing several features of the text, and emphasized and encouraged future research in working with specific textual features.

Todorov (1976), on the other hand, suggested that the concept of genre is an important classification method, even though the current genres differed from those in the past: "it is not 'genres' that have disappeared, but the genres of the past, and they have been replaced by others" (1976, 160). In his opinion, "genres exist as institution that [...] function as 'horizons of expectation' for readers, and as 'models of writing' for authors" (1976, 163). He reflected on how the labels of the genres appeared:

Let us now turn to the other term in the expression 'class of texts': class. It raises a problem only by its simplicity. One can always find a property common to two texts, and therefore put them together in one class. But is there any point in calling the result of such a union a 'genre'? I think that it would be in accord with the current usage of the word and at the same time provide a convenient and operant notion if we agreed to call 'genres' only those classes of texts that have been perceived as such in the course of history. The accounts of this perception are found most often in the discourse on genres (the meta-discursive discourse) and, in a sporadic and indirect fashion, in the texts themselves. (Todorov 1976, 162).

This quote is especially valuable for computational approaches to genres where it is common to find linguistic patterns that can distinguish random groups of texts. Todorov's proposal would mean that the researcher should only use labels that were given either by scholars (i.e. those holding the meta-discursive discourse about genres) or where the text presents itself as something different (e.g. labels on the cover).

A recent study on literary genres with a strong historical perspective was undertaken by Schröter (2019), who analyzed the case of the German *novella*. Schröter states that genres are not based on textual features and that they should be defined in the descriptive language of the Literary Studies (1–2). His analysis of the novella shows that the instances of this category of genre do not show textual or linguistic similarities, and therefore the main goal of this description should be the reconstruction of the label, both in the paratext of the texts or in theoretical works, such as poetics (36). As a consequence, the historical development of the label should be integrated in the concept of the genre by applying the family resemblance model (31–33).

In Digital Humanities, the works with the clearest historical perspective are those from Underwood (particularly 2016 and 2019 which I have already summarized in Section 2.2.5). The main question relating to this publication is Moretti's hypothesis that subgenres tend to last around 30 years. To observe this, Underwood looks more closely at the beginning and end points and the development of genre over two centuries. He uses multi-label classification for several subgenres and plots the probabilities of each text belonging to the category. The early works that show a high probability of belonging to the genre can be understood as pioneers of the genre which share inner features with the rest of the category. The genre shows high probabilities in periods of stability, or a decrease when it is altered. Underwood's results show that while some genres could fit into Moretti's hypothesis, others genres "remained stable for a century and a half " (2016).

A different tradition that mainly focuses on the historical aspect is the German-speaking tradition of Linguistics in the Romance languages: *Diskurstradition*, or *discourse tradition*. The traditional schema of the language proposed by the linguist Coseriu describes three levels: universal (the human capacity of language), historical (the historical languages), and individual (a specific text or discourse). Nevertheless, Coseriu found similar repeated patterns in many languages. Some of these were relatively simple and short (e.g. greeting traditions) and some very complex (e.g. sonnets or detective fiction). For this reason, Coseriu proposed the term *text tradition* (2007, 46, originally published in 1980), later called *discourse tradition* by Koch, who places it in a second historical level of the language, parallel or diagonal to the languages (1997, 44–46). The exact theoretical placement of this second historicity of the language in Coseriu's model has been widely discussed (Kabatek 2007; 2015; Schrott 2015, 120–24). The repetition of linguistic patterns and their modification over time would constitute shared traditions in several languages (Kabatek 2011, 97; Lebsanft and Schrott 2015, 24). This creates a bridge from the linguistic tradition to the literary texts which constitute the most common examples of these studies (Koch 1997, 46–47). An important characteristic of this concept is its aim to cover heterogeneous phenomena in an abstract model:

The concept of the discourse tradition as a cultural guideline of communicatively appropriate text organization, which was developed in Romance languages, is a central basic concept for a Cultural Linguistics. A characteristic of this concept is its wide range. Thus, under the concept of the discourse tradition very different cultural patterns of speaking and writing can be subsumed: The traditional knowledge of discourse includes communicative habits such as greetings or the opening of a conversation, but textual genres and styles of interaction also act as discourse traditions that model speaking and writing. (Schrott 2015, 115, my translation).

In addition, its primary goal is pluri-linguistic, and this corresponds to the related traditions expressed in several languages, as is the case in European literary traditions. In addition, the discourse tradition is not limited to the historical perspective, as it has been shown to have possible new perspectives in diachronic linguistics (Kabatek 2007, 331–32) and the study of cultural phenomena from a linguistic perspective.

Its great ambition has one clear issue: Since its beginning it has mainly discussed the theoretical framework and characteristics (Schrott 2015, 115), with relatively few works that actually apply it to the specific discourse tradition of several languages for long chronological periods and in defined corpora. A good example of an analysis of the discourse tradition can be found in Schrott (2015, 131–40). Schrott investigates the narrative using *imparfait narratif* in French novels, and directive questions as a form of polite requests in oral communication in French, German, and Spanish.

To fully analyze discourse traditions, it is necessary to have access to diachronic corpora in several languages, across several genres, with explicit metadata and annotation about linguistic and literary phenomena. Besides, the results should be comparable within the variance of periods and languages. This could be achieved by a closer collaboration between Linguistics and Digital Literary Studies, employing shared encoding formats (TEI XML) or annotation sets (such as EAGLES or universal PoS tags) in the different languages, genres, and periods. In this regard, the *European Literary Text Collection* (ELTeC) of the *Distant Reading COST Action* could be the first corpus suitable for analysis about the novel in the European context.

#### <IP_ADDRESS> Cross-Sectional Synchronic Studies

The other perspective on genre studies are the cross-sectional synchronic studies in which the researcher is interested in analyzing textual groups in a specific period. In these cases, historical development is not of interest because the periods are too short or because there is no expectation for the data to cover complete genre spans. In these cases, it is typical for the

researcher to consider a greater number of categories and a larger proportion of texts. This perspective is mainly taken by perspective of Corpus Linguistics, Computational Linguistics, or Digital Humanities, although some works from Literary Studies do seek an ahistorical frame of the genre.

Except for the works by Underwood (2016, 2019) mentioned previously, all the research on genres from Computational Linguistics and Digital Humanities presented in Chapter 2.2 is closer to the cross-sectional synchronic perspective. Some examples include Kessler, Numberg, and Schütze (1997), Stamatatos, Fakotakis, and Kokkinakis (2000), Santini (2011), Jockers (2013), Schöch (2013), Underwood (2014), or Hettinger et al. (2016). In all these cases, the year of publication and the historical development of the genres are of little importance and the analysis plots have no historical axis.

An interesting theoretical contribution comes from Schaeffer (1989, translated into Spanish in 2006), who proposed a historically open understanding of genres, since "the classificatory identity of a text is always open" (Schaeffer 2006, 101–2, my translation). For Schaeffer, labels are assigned in a chaotic process by several authors, creating a "patchwork lexicologique" (1989, 66) and thus the primary work of the researcher should not be in assigning new labels, but rather to "analyze the functionality of the genre names, whatever they are, and try to see to what they are referring to" (Schaeffer 2006, 53, my translation).

Textual phenomena have been studied in the field of Corpus Linguistics from several perspectives that are very similar to what Literary Studies consider genres. An important authority on this topic is Biber, who analyzed how linguistic features are distributed in different genres (or *register* in his terminology, treated mostly synonymously, see Biber 1992, 332) and the correlation between the features using multiple factor analysis. These "bottom-up statistical analyses" (Biber 2014, 7) examine correlations between linguistic features, grouping those linguistic units that tend to correlate and clustering them together in more general patterns, also called dimensions.5 A score for each dimension can be computed for each text and a mean value calculated for each genre (Biber 2014, 13). "After the statistical analysis is completed, dimensions are interpreted functionally" (Biber 2014, 11). In the following plot

5 This methodology is similar to other methods that group co-occurring or correlating features, such as topic modeling and specifically principal component analysis (PCA), although its typical units, pre-processing steps, visualizations and interpretation tend to differ.

of Figure 7, Dimension 1 is plotted with the means of different genres (vertical axis) in a chronological development (horizontal axis) over the last four centuries for English texts. The successful approach of multiple factor analysis of corpora has been tested by Biber and other researchers across dozens of languages, periods and genres. In a comparative work, Biber proposes two of these dimensions as universal dimensions of textual distinction, regardless of language, period or genre: 1) oral versus literate, and 2) narrative versus non-narrative (2014). There are several aspects from Biber's methodology that would be done differently nowadays, for example applying a shared linguistic tag set of annotations for several languages, using all the linguistic units annotated by the tool, or a more straightforward way of deciding how strongly the features should correlate to be clustered together in a dimension. Nevertheless, in my opinion, Biber pioneered the opening up of a path of statistical methodologies for linguistic features 30 years ago with many important implications for Digital Literary Studies.

In addition, in the middle of the 1980s, a similar distinction to Biber's second dimension was defined in the German-speaking Romance linguistic tradition: *Nähesprache-Distanzsprache*, or *near-language* and *distant-language*. Koch and Oesterreicher used an already existing distinction made by Söll (1985) where the medium of a text (written or oral) and its conception are separate phenomena with a certain relationship (Koch and Oesterreicher 1985). A text (e.g. a letter) or many digital formats (e.g. chats) can be written but will contain many of the typical features of oral communication. On the contrary, oral genres, such as academic discourses or sermons, are conceptually created as written texts and therefore show these characteristics. These authors integrate this distinction into the diasystem of Coseriu's Romance linguistics along the terms diatopic, diastratic, and diaphasic.

In Figure 8, the horizontal axis represents the communicative distance and the vertical axis can be understood as the frequency of the types of genres that can be found in this position, either in written (above the horizontal axis) or oral medium (below the horizontal axis). The different genres are marked with roman numbers: I) familiar conversation; II) private telephonic conversation; III) private letter; IV) work interview; V) journalistic interview; VI) sermon; VII) scientific conference; VIII) editorial; IX) law text. The model shows that the oral genres are distributed closer to near communication than the written ones, but there is a large overlapping area.

This model has been successfully adopted by many researchers in Romance languages with modifications (for example Dufter and Stark 2003). A clear caveat of the model is its lack of sharpness and standardization of visualization. In the previous figure, the position of the genres on the horizontal axis are not based on any calculation (as in Biber), but are merely hypothetical, and they do not show internal variance in each genre. In addition, the vertical and perpendicular lines are not labeled and are merely drawn to make a perfect diamond. An interesting aspect of this concept is the fact that the researchers presented a comprehensive list of linguistic phenomena in three Romance languages (French, Italian and Spanish) related to the two poles of this communicative dimension and sorted by linguistic levels (Koch and Oesterreicher 2011).

#### **2.3.5 Conclusions**

Genres and subgenres are cultural concepts with complex relationships between their main components: categories (labels), instances (texts), and features. Several models have been used or proposed to regulate the interaction between these components. The classic scholastic-structuralist model does not fit many of the characteristics of subgenre, while the family resemblance and prototype models have been proposed and only used in tempting ways. In addition, the fact that some subgenres are more similar to others has not yet found a proper formalization. Genres and subgenres can be analyzed from different perspectives: the longitudinal historical perspective, with a greater focus on the development of a few subgenres and the opinion of the authors, or the cross-sectional perspective, with a focus on many categories over a shorter period.

Any researcher must decide whether they adopt a longitudinal or crosssectional perspective. A comprehensive analysis combining both is possible but would require an enormous effort in digitizing the texts and extracting their labels. The design of the recompilation of the data and texts in this study uses a cross-sectional perspective. This is partially due to my own interest and partially due to the current situation of the digitization of Spanish literature, which is acceptable in the decades between 1880 and 1920 but shows important deficiencies in earlier and latter periods (see Section 3.1.9 and especially Figure 21).

One of the general aims of this research study is to observe how these different models and macro-models can fit the real data of the subgenres of the Spanish literature of this period, and which computational tasks can be applied. This will be done in Chapters 7.2 and 8. Aspects including hybridism, disagreement among sources, the multiplicity of labels for a single text, or

fuzzy descriptions should be treated as characteristics of the genres, finding a suitable place in the model, rather than being understood as exceptions or particularities of specific cases.

## **3. Data: Texts and Metadata**

## **3.1 Corpus of Novels of the Spanish** *Silver Age***: CoNSSA and CoNSSA-canon**

#### **3.1.1 Introduction**

In this section, I explain the steps taken to design and create the *Corpus of Novels of the Spanish Silver Age* (CoNSSA), which will be analyzed in the rest of the book. First, I discuss different issues regarding the concept of literary corpora, considering the concept of statistical population and how it can be related to literature. Second, I present the criteria for the stratification of the authors and their selection, and finally the selection of the texts. Third, specific steps about the compilation of the corpus are clarified: use of digital sources and my own digitization, encoding process, validation through XML schema and Schematron, enrichment of structure, and human validation.

This data set has been gathered for the first time. That is why one of the last sections is dedicated to the exploration of the corpus through plots and Descriptive Statistics to have a general view about its content and the typical tendencies of the novel of this period. The goal of the description through Statistics and visualization is, on the one hand, to better understand what the ranges and typical tendencies of the novel of this period are. On the other hand, I plot different values over time to comprehend the general evolution of specific phenomena over time, a point of view that, as I have already discussed, is normally abandoned by the research of this period in favor of each author's specificities (see Section 2.1.2).This part (Section 3.1.9) and one in the next chapter (Section 3.2.11) are the only ones in which a historical and not a cross-sectional perspective is adopted.1 The reason for that is that in these sections the novel is described in general and not in particular subgenres. The corpus is also briefly compared to other existing literary corpora.

1 See Section 2.3.4 for a discussion about the different perspectives about genre analysis.

#### **3.1.2 Literary Corpora**

Linguistics has been designing and collecting big amounts of texts in form of digital corpora for decades, evolving into its own subarea: Corpus Linguistics (Sinclair 1991; McEnery and Wilson 2001; Wynne 2004). This field has coined concepts such as *balance* and *representativeness*, created usual representations like *keywords in context* (KWIC), defined units like *tokens* and *types*, or proposed typologies for corpora. Most of these text collections are internally classified in subsections using information either from the text (language, genre, publishing date, form, etc.) or their producers (typically the author's gender and place of birth).

For a few years, different projects of Digital Humanities led by literary scholars have been gathering large numbers of literary texts, mainly in English and German but also in other languages. The goal of these collections is not only to make texts available to the readers, but to analyze them through digital tools and methods such as Machine Learning (classification, clustering, Deep Learning, dimensionality reduction techniques like topic modeling, stylometry, etc.), or graphs and networks.

How should the literary corpora be designed for digital methods? Since 2017, three different publications have shed light onto this question from different perspectives. The first one is a chapter by Schöch (2017) about the compilation of data sets for the Humanities, with many examples about literature. One of the most interesting aspects of this article is the question about how the data set (for example, a corpus of Spanish novels from the 19th century) reflects the actual object of the study (all Spanish novels from the 19th century), also called *population*. In each research the population must be defined considering criteria like time, place, author, form, canonization and general size of the data set. Second, the data set can be either:

The second publication, by Percillier (2017), explores specific aspects (such as scanning and OCR processing) of the compilation of literary corpora and its

quantitative analysis after the design and the criteria have already been specified.

The third publication comes from the *Review Journal for Digital Editions and Resources* (RIDE). This journal of reviews about scholarly digital editions has published two issues in 2017 and 2018 (number 6 and 7) about text collections. In their preparation, the editors Henny-Krahmer and Neuber (2017) made the criteria available for the reviewers. Its usefulness is beyond the specific frame of this journal, constituting a remarkable check-list for any new literary corpus. In comparison to the already mentioned studies, this text focuses on the importance of the documentation and transparency of the steps and decisions made, the bibliographic identification of the data set, the purpose of the collection, and its re-usability. Like Schöch (2017), they also highlight questions about the preservation and licensing of the data sets.

#### **3.1.3 Statistical Population of Authors**

As mentioned above, the first question is the definition of the statistical population: What is the object that the researcher wants to study? In my case: Which are the Spanish literary novels of this period? A population is "the entire set of people, things, or events that the researcher wishes to study" and they have to be "clearly identified on the basis of some trait" (Evans 1996, 15). A population of literature should be defined by institutions that work with literature, such as libraries, publishers, or literary scholars. From these three, only the last marks explicitly specific works as literature, for example, their treatment by manuals, monographs, and papers about literature. For publishers and libraries, literature is just a section of their material, in many cases without explicit differentiation to other textual groups like essays, history, journalistic texts, etc. For that reason, I only use manuals of Spanish literature to define the different populations that will be presented.

An even better possibility would be to use comprehensive bibliographies of the novel of this period, as Henny-Krahmer does with the novels of Cuba, Mexico, and Argentina (2017). In her research, she utilizes mainly three already published national bibliographies for the period she analyzes. As explained in Chapter 2.1, the period of the Spanish literature I analyze in this work has been treated rather disjointedly, tending to analyze either single authors or generational groups. This is probably the reason why analog general

bibliographies for the novel of the entire here analyzed period could not be found.

Instead, I decide to use the most comprehensive and finished manual of Spanish literature that covers the period between 1880 and 1939 in a homogeneous way for the population of authors: *Manual de la Literatura Española* (MdLE) by Pedraza Jiménez and Rodríguez Cáceres (1980). The traits for the definition of the population have to be mentioned in the MdLE and must fulfill the following criteria:

	- o novels, that means prose (not written in verse, not to be performed)2 fictional works,
	- o as independent publication (i.e. a book),
	- o in Spanish,
	- o between 1880 and 1939.

That means that all authors who only wrote theater, poetry, essay, or journalistic texts are excluded from the population. 107 authors fulfill3 the criteria and for each of them, the following information is gathered: A full and short version of their name, year of birth and death, gender, amount of dedicated pages in the MdLE, the genre (besides the novel) that the author wrote the most in, and links to searches for the author in digital sources such as the Spanish National Library (BNE), ePubLibre, Archive.org, Cervantes Virtual, Wikisource, Gutenberg Project and Google Books.

This data allowed me to analyze the state of the digitization of this period (Calvo Tello 2017). One of the most important results of this work was to observe that both canonization status and year of the death of the author have moderate correlations with the state of the digitization of the author.4 This information is important because it will have consequences for the collection of the texts and the definition of the corpus.

2 I do include works that were written in dialogue but are treated and understood as novels (for example *La casa de Aizgorri* or *Paradox, Rey* by Baroja, or *La razón de la sinrazón* or *El abuelo* by Galdós). See Section 2.1.4 for a discussion

3 135 authors wrote some kind of prose.

4 It also shows that the most useful projects to collect texts for quantitative research were Cervantes Virtual, Epub Libre and Archive.org.

The chronological criteria to filter authors does not refer to their lifespan, but to whether their works were published between 1880 and 1939. How does that affect the distribution of the different generations in the population? Is there a homogeneous distribution? Are there many authors that are either much younger or much older in comparison to most authors? To explore that, the decades of the authors' birth and death are plotted in Figure 9.

*Figure 9: Bar plot of birth of authors*

Figure 9 shows that the actual span of dates of birth represents a century, with a distribution skewed to the right with a median5 of 1881 and a variance, in Interquantile Range (IQR),6 of 28 years. That means that half of the authors were born between 1853 and 1909. The older authors are over 50 by the year 1880 (the oldest one is José Selgas Carrasco, born in 1822, followed by Juan Valera in 1824), while the youngest author was born in 1917 (Rafael García Serrano). It is surprising that no author born in the 1920s got to publish their first novel before 1939. One of the reasons younger writers might be underrepresented could be explained with the bar plot of Figure 10, with the values of the year of death of the authors:

5 The median is the "middle score in a distribution" and is a better central tendency than the mean in non-Gaussian distribution (Evans 1996, 69–80).

6 "The interquartile range is the range of values that includes the middle 50% of the distribution" (Evans 1996, 83).

*Figure 10: Bar plot of deaths over decades*

The distribution of Figure 10 resemblances the previous one, with two unexpected high values in the 1930s and 1940s. The median is the year 1945, while the mode7 of the decades is 1930, with an IQR of 31.5 years, meaning that half of the authors died between 1913 and 1977. Because the average span of life is about 70 years, the decades around 1950 would have been expected to be the ones with the highest values. The reason for this abnormality is arguably the Civil War and its repercussions during the Francoist dictatorship (for example, Miguel Hernández died in prison in 1942). This could also explain why no young writer published novels during the decade of the 1930s: after 1936, the cultural branch could have been affected by the war, a hypothesis that will be tackled later. The span of dates of deaths is even longer than the one of births, covering around 120 years. One interesting aspect is the fact that some authors died shortly after 1880: Selgas Carrasco died in 1882 at 60 years, Rosalía de Castro in 1885 at 48. Therefore, some writers are markedly older than the rest, which means that this population, and therefore the corpus, contains authors related to previous literary periods, and it can be the explanation for some abnormalities observed in Section 8.6.8

7 "The most frequently occurring value in a distribution" (Evans 1996, 69).

8 This raises the question if the population should not have two chronological criteria for more homogeneous corpora: one about the author (born after 1839), and another about the works (published between 1880 and 1939).

#### **3.1.4 Statistical Population of Prose**

Now that there is a population of authors, the next step is towards a population of texts. Ideally, a population of novels would be the goal now. But as I will clarify, some texts do not show a clear assignment to their main genre: It is unclear whether they are novels, essays, or texts without any general genre (*genreless texts* in Derrida's terminology). That is why, I will first define a population of prose written by the authors defined in the last section. These texts will be analyzed in the following section with the purpose of obtaining a population of novels.

This population is constituted by all prose texts mentioned in the MdLE published between 1880 and 1939 by the authors from the population of authors. From these works, I gather the following information: name of the text, year of publication, main genre9 (i.e., *novela, cuentos*...), subgenre and dedicated number of pages in the MdLE. This manual contains a total of 1,426 prose works from the authors mentioned in the previous section. From these works, the main genre provided by MdLE is *novela* (English 'novel') with 51% of cases. The next most frequent labels are *ensayo* ('essay') with 15%, *cuentos* ('short stories') with 10%, and *novela corta* ('short novel') with 6%. A group of 62 texts (4% of the population) presents an unclear primary genre, which will be discussed again in the composition of the corpus. Apart from these cases, 14 more labels cover the remaining 34 texts (2% of the population), including, among others: *biografía, crónica, autobiografía, prosa lírica, estampa, greguería, diálogo, memoria, libro infantil, diario, aforismo.*

In the different fields working with corpora, a recurrent criterion for the quality of a text collection is its balance across categories such as genre, decade, author, etc. I have already shown that the prose in the MdLE is extremely skewed: some categories like novel are much more frequent than others, like the essay. Now, I want to examine how do the prose works relate to the different decades. In the following plot of Figure 11, the number of texts of each kind of text is shown by decade. The number of publications analyzed by the MdLE increases constantly. It actually triples from the 1880s to the 1920s, with a remarkable drop in the 1930s, an issue I will address in the next section. What Figure 11 already indicates is the fact that the population of

9 In some cases the MdLE only assigns the genre indirectly, for example, grouping all novels in the same subsections of an author with a reference to the genre in its header.

prose10 it is not balanced over decades: There are decades with many more texts than others. Trying to create corpora that are balanced chronologically is to distort the object of analysis.

#### **3.1.5 Statistical Population of Novels**

From the population of prose, now I am moving to the population of novels: The MdLE contains 727 texts labeled as novels and 62 prose works without any clear information about its genre.That means, the number of novels in the MdLE could be between 727 and 789. In Figure 12, the number of novels treated by the MdLE is plotted over decades. The data of Figure 12 shows a similar distribution of novels over decades as Figure 11, increasing constantly until it reaches its peak in the 1920s: As it happened with the prose, different decades contain very different numbers of novels. The 1930s show again a considerable drop in comparison to the previous decade. I have already mentioned the expected effect that the Civil War could have in the cultural branch, but, is

10 Of course, these values do not represent the total amount of new prose works published over decades, it only exhibits how many works are considered as part of literature, represented by the MdLE.

*Figure 12: Bar plot of novels by decades*

the war the sole explanation? To shed some light on this question, I plot the number of novels per year:

*Figure 13: Number of novels over years*

The number of published novels increases constantly until 1910, when one of the highest peaks of the whole period can be observed. The amount stabilizes during the 1910s and increases again at the beginning of the 1920s. What is interesting is that the fall starts before the war breaks out, with decreases between 1933 and 1935. This pattern deteriorates with the start of the war in 1936, with the exceptional low number of only two novels in 1937.11 Why are there notably fewer novels being published at the beginning of the

11 Martínez Cachero has analyzed the "sterile period of the war" for literature (1984).

1930s than the previous decade? The data gathered for this analysis does not yield a response for that. Ródenas de Moya has defended the idea about the fatigue of the novel in the late years of the Spanish avant-garde (1998, 137; 2000, 67), which could explain the drop at the beginning of the 1930s.12The texts marked as novels in the MdLE have been identified. The next step is to observe whether this manual assigns a further label to them, a subgenre, like erotic novel, adventure novel, or naturalist novel.13 Out of 727, a subgenre label can be identified for 349 cases. In total, this manual uses 88 labels, the most frequent being *novela erótica* (erotic novel) with 38 works (5% of the novel population). This label is followed by five labels between 30 and eight works: *episodio nacional, histórica, naturalista, social, realista, de aventuras*, and *humorística*.The first of this group has been already discussed in the Chapter 2.1 as one of the one-author-labels, similar to historical novels, coined and employed by Galdós. The rest of the labels can be accepted as unproblematic types of novels. After these, there is a long tail of dozens of different labels with very few texts populating them, and many of them are disputable: *de la carne* ('about the flesh'), *de la vida* ('about life'), *sevillanas* ('from Seville'), *sentimental, valenciana* ('from Valencia'), *lírica, de transición* ('of transition'), etc.

At the end of this long tail of the frequency of labels in the MdLE, 65 of the 88 different labels are used for up to three texts. In fact, there are 36 labels that are assigned to a single novel: Either all these are extraordinary single creative works that founded and ended a subgenre, or it is a too differentiated use of labels to categorize texts. That means, the MdLE has a strong tendency to use very specific labels for very few texts with a hand-full of exceptions like erotic, historical, or naturalist novel.

As I have already shown, the several decades contain different numbers of novels. It can now be expected that the different subgenres should be equally distributed throughout the decades in the corpus. Consequently, it should have the same or a similar number of erotic novels, for example, in each decade. But, how is the original data of the population distributed? Does it depart from a balanced situation? To observe this, I plot the most frequent subgenres from the MdLE over decades as a heat map in Figure 14.

12 "After 1931, the impression that the novelist renewal enterprise undertaken around 1923 had been ruined took over most of those who had invested their artistic capital in it" (Ródenas de Moya 1998, 137, my translation)

13 In Chapter 3.2 I will present the remaining of the sources, which will be used for the rest of the research study, with its main analysis in Chapter 5.1.

#### *Figure 14: Heat map of correlation between subgenres and decades*

The distribution of subgenres over decades in Figure 14 shows a tendency to contain one or two subgenres that are far more common than the rest during any given decade. This could be expected from labels like naturalist novel, which can be understood as a term that describes a literary period: The naturalist novel would be the novel published at the end of the 19th century. Indeed, naturalist novels are more frequent in the 1880s than in the 1920s or the 1930s, although its frequency in the 1910s is also one of the highest. The most important aspect of this figure is to observe that the naturalist novel does not have a stronger association with a certain decade than the rest. Other subgenres that are not so strongly associated with a literary period do show clear peaks in some decades, such as the social novel in the 1910s, the erotic novel in the 1900s and 1910s, or the historical novel in the 1920s.

The subgenres show correlations with other variables that make it difficult, in many cases even impossible, to balance them and have similar numbers of texts. I have shown why it is not possible to balance subgenres through chronology. As I will discuss at the end of this chapter, this is also the case for the author's gender. This "undesired correlation" (Schöch 2017a, 226) could be seen as an intrinsic phenomenon between categories of literary (or even humanistic) data. Another variable that also shows a strong association with the subgenres is the author, a category that is plotted (only the five most frequent labels) in the heat map of Figure 15.

#### *Figure 15: Heat map of correlation between subgenres and authors*

The most common case is to find one or a few authors that are very predominant in the subgenre, with the extreme case of the one-author-label of the *episodio nacional* by Galdós. As Figure 15 shows, the MdLE strongly associates subgenres and authors. Any attempt of trying to balance subgenres and authors would lead to maintain only very few instances, if any at all. The relation between author and genre will be further analyzed in Chapters 5.1, 5.3, and 6.2.

#### **3.1.6 Definition of the Corpora: CoNSSA and CoNSSA-canon**

For now, several populations have been discussed and presented. These are the preparatory steps towards a reasoned creation of the corpus. A fundamental decision about the process is the relationship between the statistical population and the actual sample.The standard method in Statistics is *random sampling*, a process that selects instances from a given population by chance (Evans 1996, 197) to "maximize the sample's chance of accurately representing the essential characteristics of the population […] so that results and conclusions derived from the sample can be validly generalized to the population" (Evans 1996, 22). On this random sample, the researcher can then use Inferential Statistics, "more advanced procedures [than Descriptive Statistics] that enable us to generalize from sample data to population characteristics" (Evans 1996, 26). This method would require to randomly sample a specific number of texts from the population of novels of the MdLE, without considering wether these texts have already been digitized or not. To do that, projects would need to be able to digitize hundreds of texts, regardless of accessibility or difficulty, which is probably one of the reasons why random sampling from a population is extremely rare in the Humanities (digital or not).That is also the reason why I cannot apply it in my research.

The second possible strategy is to create a stratified corpus following the balance of the possible combinations of criteria in the statistical population (Schöch 2017a). This strategy requires to know the criteria that affect the object of study, describe them in the statistical population, and calculate the number of instances in each possible combination of the values. This would have meant in my case the following steps: First, select a number of subgenres that should be studied. Second, obtain the subgenre labels of the entire population. Third, list the criteria that affect the object of study, the subgenre.14 Forth, obtain these values for the whole population. Fifth, calculate the combination of these values (cells) and get their proportion (i.e., size in the entire population). Sixth, decide a number for the size of the stratified sample. And finally, select (either randomly or in an opportunistic way) the texts that should populate the cells. There are several problems to implement this strategy in the case of my study: It is unclear which subgenres should be considered

14 An open number of aspects like the author's gender, literary genre, canonization, author, year of publication, information about the characters, the plot, the narrator, the setting, etc.

(a question that will be analyzed in Chapter 5.2), which texts are assigned to which subgenre, and whether this assignment should be in categorical terms or in degrees (as in the prototype theory, see Section <IP_ADDRESS>). In addition, it is unknown which criteria affect subgenres and their information to the entire population is inaccessible. And finally, since the classes have strong dependencies as the previous figures showed, it can become impossible to balance all the different aspects.

Since the two standard options seem unfeasible, I decide to follow an alternative two-fold strategy. My goal is to create two different versions of the same corpus, in other words, two samples of the same population, one embedded into the other. To better understand this strategy, I give two examples from different types of instances. The population of Europe can be divided into the different national populations, which itself can be divided into their units: in the case of Germany, the *Bundesländer.* The population of Bavaria, for example, can be divided into their constituent units (cities and regions) and this way we can go deeper until we reach a population so small that a researcher can interview all of its inhabitants, for example, a building. Of course, the smaller the population is, the fewer instances our results will cover and probably the less interesting our outcome will be.

Another example, using now a textual case, is the Bible. This text can be understood as a whole population of verses, chapters, or books the researcher is interested in. Inside, there are two main groups of books: The Old and the New Testament, that the investigator can understand also as populations, populations within a greater population. Inside, there are smaller groups of books like the Gospels, Pentateuch, Paul's letters, that, again, can be seen as a third level of populations. A step further, each book can be seen as a whole population of verses. Depending on their goal, the researcher can choose any population (from the whole Bible to a single book), and then decide whether they wants random samples from it, or get the data from the entire population. In the first case, the researcher would use Inferential Statistics in the sample to get generalizations about the entire population; in the second case, they would use Descriptive Statistics since the entire population is available.

This provides a third possibility for the Humanities15 besides random and stratified samples: obtain the entire population that can be completely digitized. The fact that for social and medical sciences the study of smaller pop-

15 For other areas, such as medicine there are other possibilities such as random assignment.

ulations is normally not the goal (Evans 1996, 14), can differ for the Humanities. In fields like Literature, Art History,Theology, or Philosophy (and in many cases in Linguistics), the most common goal is to analyze relatively small data sets (not greater than a hundred cases) with instances that had a remarkable impact on culture. The sample would contain all instances of the population; in other words, population and sample are the same and therefore the researchers can use Descriptive Statistics in their data (Evans 1996, 25–26).

For the case of the Spanish novel between 1880 and 1939, the largest population of texts I am considering is the prose works mentioned in the MdLE. I do not argue that this population is the greatest of its kind. Other possibilities could imply trying to obtain all novels mentioned in at least one manual of literature (from a closed list), or even all original novels published in Spain by Spanish authors between 1880 and 1939. These could be seen as the largest population of novels of the period, and despite their great interest, the costs of obtaining them would be too large for a dissertation. Therefore, I am using as a base the largest documented population that I have access to.

From this population of prose works mentioned in the MdLE, I create a smaller population of canonized novels, following the criteria that will be explained in the next section. This smaller population is called *CoNSSA-canon* and it neither contains all the novels of the period, nor all the novels mentioned in the MdLE, but it does contain all the most canonized novels. That is why the CoNSSA-canon is an interesting object of study to analyze this part of the Spanish literature: It can be understood as a corpus that exactly maps one statistical population of canonized novels, and therefore can be analyzed through Descriptive Statistics.

That is the first of the two corpora that my strategy seeks. I design another sample also based on the statistical population of novels in the MdLE, called *CoNSSA.* This one is an opportunistic sample in its size (it contains as many texts as possible) but stratified through authorship, canonization, and decades. The advantage of having CoNSSA and CoNSSA-canon is that I can test hypotheses in both, and see whether a specific tendency is only perceptible in the canonized population, only in the opportunistic sample (and therefore possibly a result of the bias of compilation), or in both; if so, it would be reasonable to think that it is a general pattern of the novels of this period. The population of prose in the MdLE contains CoNSSA, and CoNSSA contains CoNSSA-canon, similarly to the example of the people of Europe of the verses in the Bible. To understand the exact relations and number of texts better, they can be plotted as in Figure 16.

*Figure 16: Representation of number of texts in the corpora and populations*

All the texts are contained in the largest population I am considering: the prose population of the MdLE, with 1,426 texts. From this population, I gathered 358 texts, which constitute the corpus CoNSSA, in general an opportunistic corpus with certain stratification, as will be explained in the following section. It contains a total of 344 novels, which constitute around 47.3% of the total of novels in the population of prose. Inside CoNSSA, there are 136 that can be understood as the most canonized novels, which constitute the CoNSSA-canon, a corpus that maps the statistical population of the canonized novels. At the same time, the remaining 206 novels and a group of 14 texts with unclear genre make up the rest of the CoNSSA.

#### **3.1.7 Criteria for Selection**

In this section, I want to specify the exact criteria that the texts need to exhibit to be considered part of the canon of the novels, and therefore be eligible to form part of the CoNSSA-canon. Each novel has to fulfill both criteria:

These criteria cover a minimal consensus on both, and retain only those works that occupy more space in terms of pages. There are two reasons why the MdLE and not the HdLE appears in both criteria: first, this manual is the most comprehensive; second, it is substantially more structured than the HdLE.16Once the definition of CoNSSA-canon is concluded, I design the stratification of CoNSSA. As already mentioned, the canonization has a measurable correlation with the state of digitization of the author (Calvo Tello 2017). Besides, canonization reflects how important the author is for Literary Studies. These are one practical and one theoretical reason to use canonization as the most important criterion of composition of CoNSSA: The corpus contains a larger proportion of the production of the most canonized authors. To formalize that, I need the total number of novels written by each author, plus the canonization status of both author and text. For this, I use again the number of pages used in the MdLE, which is organized in six ranges. To each of these, I assigned a minimum proportion of novel production that the corpus needs to include from each author:

16 While the information about the pages for each novel is to be found in the ToC of the MdLE, the HdLE is rather structured as an essay, going back and forth between different texts, authors, and topics.

*Table 1: Number of pages and minimum percentages of each author's work in the corpus*

These percentages are chosen to try to maximize the number of novels in the corpus, considering also the limitations of the project. Authors with fewer than ten pages tend to be digitized very poorly, and the manuals cover the information about their texts scarcely. In fact, most texts of the lower ranges were digitized manually, a costly step in terms of money and time.

There were some extra criteria applied to a few authors for specific historical reasons:

• *Baza de Espadas*, by Valle-Inclán, was not published as a book during its authors life, but only in magazines in 1932, his sole novel in that decade.

All these texts are marked explicitly as exceptions in their metadata and none of them are part of the CoNSSA-canon, which does not contain any exception. Thus, they can be automatically ignored or used for specific purposes.

The 14 works whose genre is unclear also belong exclusively to CoNSSA, and they contain some of the most influential works of this period, such as *La lámpara maravillosa* and *La media noche* by Valle-Inclán, *Platero y yo* and *Diario de un poeta* by Ramón Jiménez, *De un cancionero apócrifo* and *Juan de Mairena* by Antonio Machado, or two editions of the *Greguerías* by Gómez de la Serna. Some of them contain certain chapters belonging clearly to one genre, and others to different ones (such as poetry and prose, like in *De un cancionero apócrifo* or *Diario de un poeta*). But the majority of them represent an intrinsic mix of genres across chapters. The main genre of these works will be analyzed in Chapter 3.3.

Once the number of novels per author was defined, I have to choose which novels of each author should be digitized for the CoNSSA. In this step, I could have just followed practical criteria about the digitized status, or the price of editions. Instead, I tried to define several criteria in order to increase the representativity for the entire production of the author. These criteria are the following:

The second criterion seeks to cover the different styles from one and the same author, especially as far as the very prolific ones are concerned. For example, Baroja wrote in different genres (adventures, dialogue, historical, sentimental), grouping them together in different *series* (*La lucha por la vida, La raza, Las ciudades, Memorias de un hombre de acción…*), and his writing stretches over four decades of the analyzed period (1900-1939). The selected texts for the CoNSSA intend to represent all these facets.

In some cases, the previous criteria are not enough to choose one text over another. In those cases, I considered the digitized status and format, favoring the following aspects:

#### 114 The Novel in the Spanish Silver Age

*Figure 17: Distribution over decades of the population, CoNSSA and CoNSSA-canon*

Figure 17 shows the distribution of population of novels in the MdLE, CoN-SSA and CoNSSA-canon over decades. The lengths of the bars of CoNSSAcanon and CoNSSA are more similar between them, while the population of novels shows a different tendency, with the highest peak in the 1920s. For its part, the CoNSSA-canon has a peak in the decades of the 1880s and the 1900s. This shows that, while more texts published in the 1920s are treated by Literary Studies, the most influential works were published either in the 1880s or the 1900s. Depending on whether the researcher is using quantitative or qualitative criteria, they will find different shapes of corpora. The distribution of CoNSSA seems closer to the population of novels, though it displays a plateau between 1900 and 1920. The reason for that difference between the total population and the CoNSSA is the difficulty of accessing digitized texts published after 1910, something that will be explained in the following section.

### **3.1.8 Digitization Steps**

The corpus is encoded in XML (as recommended in Percillier 2017) using the Text Encoding Initiate (TEI, recommended in Agenjo 2015; Schöch 2017). The step between the input format (normally HTML or similar) to TEI is implemented in Python scripts using mainly regular expressions. In the cases where no markup is found, I used PDF documents processed with the OCR Software by the company Abbyy (FineReader and Recognition Server). They were then exported as HTML files,17 joining the rest of the workflow mentioned before.

Once the TEI XML is well-formed, the elements *div* were encoded with a type attribute to distinguish the different levels of text units, with the following possible values:

The Table of Content of the digital source was compared to the digital Table of Content of the XML file (called *outlier* in the program Oxygen XML Editor) to control whether the file has lost spans of text during the conversion. In the cases in which the text was digitized by the project, the PDF was also controlled to avoid mistakes and mark textual elements, such as poems or floating texts.

After that, the metadata of the texts were manually assigned. An abstract of the book is copied (if available, quoting the source), or composed (if I personally read the book) in the *abstract* element of the header. From this infor-

17 To be more specific, headers and footers were not exported, but the CSS information was exported in order to use it for the conversion into TEI.

18 In some cases, it can also contain sections. The divs inside a floatingText were also marked as sections.

19 Normally starting with a header, in many cases sequential.

20 Just in some cases, like the novel El laberinto de las sirenas, by Baroja.

mation, the metadata about the text is assigned, an aspect that will be discussed in Chapter 3.2. A script measured specific area aspects of the text and TEI elements, placing this information in the header as well. Finally, the file was validated using an XML Schema and a Schematron, which is published as GitHub repository.

#### **3.1.9 Description of the Corpus**

In this section, several aspects of CoNSSA and CoNSSA-canon will be described. Firstly, because these data sets have been gathered for the first time, so a brief description can be useful to delimit the results of this research and compare it to other corpora. But an even more important reason that will be crucial in Chapter 3.2 is that a description of CoNSSA and CoNSSA-canon offers a sharper picture of the novel of this period. The goal of this research study is to analyze the subgenres of these novels. By knowing what the ranges and central tendencies of the novel during this period are, further ahead, it will be easier to identify whether specific features are just part of the evolution of the novel during the *silver age*, or whether they are specific for a given subgenre.

As already mentioned, the CoNSSA corpus contains 358 texts, with a total of 344 cases clearly belonging to the novel. This data set represents 47% of the population of novels as described in Section 3.1.5. It contains 26.7 million tokens, including 4 million typographical ones. In contrast, CoNSSA-canon contains 137 texts, 19% of the total of the population of novels, with 11.5 million tokens (including 1.8 million typographical ones).

The proportion of the texts per author following its canonization (as described in Table 1) was achieved, as shown in Figure 18.

The more pages the MdLE uses for an author, the higher is the proportion of their works in the corpus. However, for many writers that means 100% of their novels, especially for either those authors with more than 40 pages in the MdLE or who wrote five works or fewer. The median of coverage per author is 86%, with an IQR of 50%. The two data points in the above scatter plot on the right bottom corner are canonized author with a lower proportion than 80%. These exceptionally low values are the unusual cases of Baroja and Galdós, who, as already explained, produced an extraordinary large number of works and their influence in the corpus had to be controlled.In the last section of this chapter, I will report on the difficulty of balance of author's gender in the

corpus. I have already shown how categories such as subgenres, authors, or decades are not evenly distributed. Actually, there are no reasons to assume that other categories will be distributed equally. That is also the case when the geographical distribution of authors is analyzed. The information about the birth and death of the authors can be plotted over a map of the country to understand its geographical dispersion better, and to know which regions are over or underrepresented. For that, I used the information available in Wikidata (using the identifiers from the Spanish National Library), converted the places of birth and death to geolocations, and plotted them in a map using

the DARIAH Geo-Browser.21 In the next figure, the birthplaces of the authors are shown in orange, and their place of death in purple:

*Figure 19: Place of birth (orange) and death (purple) of authors of the corpus. Detail around Spain.*

In this map, certain historical phenomena become evident: Madrid is the place where most authors are born, and even more so, where they tend to end their days. On the other side, other regions are clearly underrepresented, especially Catalonia. The geographical places of birth and death are two other examples that demonstrate that almost nothing is balanced in a literary corpus, and that it is reasonable to expect that other sociological variables will behave similarly.22 What is the distribution of texts per author? Each author has two texts in median, a mode of one, and an IQR of four. That means, half of the authors have between one and six texts in the corpus, being the most typical case a single work. There are 14 exceptionally prolific authors of this

21 Under following link, the data can be visualized interactively in the Geo-Browser: http s://geobrowser.de.dariah.eu/?csv1=https://cdstar.de.dariah.eu/dariah/EAEA0-BAFE-592 8-4084-0&csv2=https://cdstar.de.dariah.eu/dariah/EAEA0-10BC-53B8-80DA-0.

22 Mainer mentions the fact that most authors came from the middle class, with very few cases of authors who were born in lower social strata (Mainer 2010, 148–49).

period: Azorín, Baroja, Bazán, Blasco Ibáñez, Concha Espina, Galdós, Lanza, Miró, Pereda, Pérez de Ayala, Serna, Valdés, Valle-Inclán, and Fernández Flórez.

Figure 17 with the distribution of the population of novels, CoNSSA, and CoNSSA-canon over decades, shows that it has not been possible to mantain in CoNSSA the high peak during the 1920s that is visible in the population. To explain this failure, Figure 20 shows the association between sources of digitized texts and decades:

*Figure 20: Amount of texts over decades, distinguishing source of text*

While the great majority of texts of the first decades have been extracted from Cervantes Virtual and Gutenberg Project, those numbers descend notably with time. Already for the 1900s, ePubLibre becomes one of the most important sources, moving towards my own digitization (Abbyy) in the 1920s. Almost all texts of the 1930s come either from ePubLibre or from my own digitization. In order to represent in CoNSSA the high peak of the 1920s that is perceivable in the population, it would have been necessary to digitize another 100 novels, something the project was not able to carry out. A look at the former heat map shows that, for this period of 60 years, the most useful sources change completely: while Cervantes Virtual and Gutenberg Project are enough to analyze the 19th century, they are entirely insufficient for the 20th century. The explanation for this change is, on the one side, the copyright, which in Spain lasts 80 years after the author's death: The industrial rights

of writers who died in 1936 expired in 2017. The later a text was published, the more probable is that its author still retains the copyright. On the other side, Gutenberg Project and Cervantes Virtual are projects which started some decades ago, meaning their legal limits are not representative of the current frontier of copyright, but rather of that some decades earlier.

Another aspect that I would like to observe is the standard values of some units of the novels, such as tokens and chapters: What is the expected length of novels of the *silver age*, both in tokens and chapters? What are the central tendencies and how do they typically deviate? Are 19th-century novels longer or shorter than the ones from the 20th century? The length in tokens as a histogram is shown in Figure 21.

*Figure 21: Histogram of number of tokens*

The historgam shows a Poisson distribution with a median around 60,000 tokens and an IQR of 47,000 tokens. To compare these values to known texts of the period, *Juan de Mairena* by Machado (57,000 tokens) or *El árbol de la ciencia* by Baroja (63,000 tokens) are close to this central tendency. The shortest text still labeled as a novel (*Erika ante el invierno*, 1930, by Francisco de Ayala) has less than 4,000 tokens, while the longest (first volume of *Araña Negra*, 1892, by Blasco Ibáñez) has 252,000 tokens.23 It is not surprising to see that the

23 Overall, there were three novels with extreme values regarding their length: *La Regenta* by Clarín, *Fortunata y Jacinta* by Galdós and *Araña Negra* by Blasco Ibáñez. These three

longest novel was published in the 19th century, while the shortest one is from the 1930s. In fact, novels become overall shorter during this period, which can be observed in the next scatter plot. This tendency takes place both in the CoNSSA (Pearson's r = - 0.24\*\*\*)24 and even stronger in the CoNSSA-canon (Pearson's r = - 0.45\*\*\*). In the case of CoNSSA, the novels become 562 tokens shorter every year on average (that means, that is the slope of the regression analysis) on a regression analysis, while the reduction for the CoNSSA-canon is of 989 characters annually. More details can be found in the Jupyter Notebook.

Figure 22 shows a measurable effect that affects the novel of this period, which can be observed using only basic data such as the novels' year of publication, and the length of the text in tokens: The novels of this period become statistically shorter, with this tendency being even more distinct in the case of the canonized texts. That means, texts belonging to typical subgenres of the 19th century (such as realist or naturalist novels) are expected to be longer than the ones belonging to categories of the end of the period (such as social or comedy novels). Besides the results about the data, this shows how the strategy with the nested corpus can operate: the two versions of the same corpus can be used to evaluate a phenomenon, paying attention to whether it is observable only in the opportunistic and bigger version, or also in the smaller, better controlled canonized population. Ródenas de Moya postulated that prose texts of the later period become shorter over time (2000, 49), but this analysis shows that this process actually starts before the 1930s, and that it is a general pattern in novels.

The entire text is not the only textual unit that becomes shorter in this period. A similar effect is observable at the level of sentences. To measure this, I calculated the median length of sentences of each novel in tokens. After that, I created a data point for each novel and plotted it as a scatter plot in Figure 23.

Figure 23 shows that sentences become shorter during the entire period. In fact, the slope shows that they become one character shorter annually,

texts were actually published in different volumes and even each individual volume still constitutes some of the longest novels of the corpus. Considering the particularities of their publication and with the aim of having a more homogeneous length of texts, these three novels are represented in the corpus by their volumes: two for *La Regenta* and *Araña Negra*, and four for *Fortunata y Jacinta*.

24 I follow the convention of the social sciences of using one asterisk if the p-value is smaller than α 5%; two asterisks if < 1% and three asterisks if < 0.1%.

*Figure 22: Scatter plot with regression line of length of novels in tokens over years*

and this happens both in the entire version of the corpus CoNSSA (slope of - 0.97\*\*\* characters per year), and in the smaller CoNSSA-canon (slope of - 1.2\*\*\* characters per year).

A textual element that has been explicitly annotated in the corpus is poetry. The median number of poems in the novels is one, with an IQR of four. This means that, although there is often no poem at all in a novel, embedded poetry is not an exception but actually the norm. That is an unexpected result that raises further questions: Are these poems original compositions of the author? Are they quotations from other works? If so, from which periods, authors, and languages? Does the style of these poems resemble the poetry published during this period? These questions are beyond the scope of this research, but they highlight the advantages of well-structured corpora and their statistical description.

*Figure 23: Scatter plot of the length of sentences (median) over time in CoNSSA*

As already mentioned, the corpus contains more than 20 million tokens. If this value is compared to other literary corpora in Spanish, the result can be seen in Figure 24.

As it can be seen in Figure 24, IMPACT (novels of the Golden Age), TESO (theater of the same period) and CoNSSA-canon are quite similar in number of tokens, with sizes around 10 million tokens. The complete version of CoNSSA duplicates that. Two of the corpora created by the Real Academia de la Lengua (RAE), CREA and CORDE, comprise important parts of literary texts.25 CORDE specifies the exact amount of tokens used for narrative prose: about 31 million. CREA only reports the number of tokens for a broader

25 The last corpus of the Academia, CORPES, does not contain a section of literary text, therefore it does not appear in the illustration.

*Figure 24: Number of tokens (millions) in Spanish literary corpora, distinguish format (purple = TEI XML, blue = XHTML; yellow = plain text)*

category, that is, fiction: about 28 million. That means that CoNSSA represents around two-thirds of the biggest literary corpora in Spanish.That makes the CoNSSA one of the largest resources of literary texts in Spanish, and the largest corpus for Spanish novels, encoded in TEI XML, as well as for the period between 1880 and 1939.

Thanks to this description of CoNSSA, one of the largest corpora of literary texts in Spanish, a sharper picture of the novel of this period is now available: The basic metadata of texts and authors show strong associations, the mean novel of this period has 60,000 tokens, it tends to have poetry embedded, and both texts and sentences became consistently shorter over this period.

#### **3.1.10 Gender Distribution**

One of the expected requirements for corpora in the last decades is a balance between genders: Texts by female and male authors should represent similar or equal proportions of the corpus to be representative for their societies. As I have explained, my corpus follows the statistical population obtained in the MdLE. What does the gender balance look like in this source? From the 107 authors of novels analyzed for the period between 1880 and 1939 by the MdLE, 100 are male and only seven are female: Bazán, de Burgos, de Castro, Chacel, Concha Espina, Mulder, and Sinués. That means that the literary source for the population of authors contains only 6.5% of female authors, leaving 93.5% for male authors. The number of men is clearly overrepresented in this manual.

But what about the female authors that did get into the canon? Are they undervalued in comparison to men? Are they somehow treated as less important authors? To answer that, I again employ the number of pages dedicated to each author as an approximation for canon: The more space literary scholars utilized to discuss an author, the more important she or he is, the more canonized.26 In Figure 25, the authors are ordered by the number of pages, with the gender encoded in the color of the bar.27

Although no female author is in the top ten, Bazán at least makes it in the top twenty. Then there are female authors in the medium range of the canon (de Castro, Chacel, and Concha Espina) and finally some among the less canonized writers (not shown in the figure: de Burgos, Mulder, and Sinués). For both genders, the MdLE has a median of 5 pages.28 Therefore, the data shows that it is harder for women to get into the canon, but once they do, they are treated similar to men, at least in the MdLE.

For now, I am analyzing the gender distribution in the population of authors, but how does it translate into the population of novels? And how do these values change over time?

In total, male authors wrote 673 novels (92.6%) in contrast to the 54 texts by female ones (7.4%). Figure 26 shows that the total number of works by female authors over the decades remains stable, meaning that the proportion

26 Similar quantitative approaches have been already used by different scholars.

27 I set a cutoff of more than three pages in order to get a figure that fits the page, with 64 authors.

28 IQR of 13 for female authors, 21.5 for male.

*Figure 25: Amount of pages in MdLE for each author (>4 pages)*

*Figure 26: Bar plot of novels over decades differentiating author's gender*

becomes smaller: While in the 1880s female authors write about 15% of the novels, this number drops to 6% in the 1920s and 1930s.29 The population does not get more balanced in terms of the author's gender in the latter decades, quite the opposite.This is even more surprising if we consider the tremendous

29 These values are strongly affected by the fact that Bazán wrote a large amount of works at the end of the 19th century.

advance in female literacy that took place in those years (de Gabriel Fernández 1997): More women were learning to write and read, but they had fewer chances to get into the literary canon. Since I am using this data as the base for the CoNSSA, it will also underrepresent female authors. In fact, 8.1% of the texts in CoNSSA are written by women, a similar proportion to the one from the MdLE, although slightly higher.

In a recent paper, the researcher Juana María González has studied the journal *Índice literario*, which published reviews of literary works between 1932 and 1936 (González 2021). One of the aspects that she analyzes is the distribution of the authors' gender, arguing that this journal was a progressive publication with women among the publishing board. However, in her study González finds that the number of texts written by women reviewed in this journal is surprisingly low: around 6% in total, 11% in the case of the novels. As it can be observed, the distribution in the MdLE and in the CoNSSA are similar to these results.

There is a clear gender bias, but who exactly is preferring men to women? Who is the source of this injustice? The male and female researchers who wrote the MdLE? I? Researchers of literature in general? Only the ones from Spain? Is it a general problem of Academia? The publishing houses of those decades? Cultural agents who influenced the canonization process? The readers? Their sense of literariness or literary quality (cfr. Koolen 2018)? These questions are definitely beyond the reach of this publication, but one fact is certain: What is currently considered the Spanish novel between 1880 and 1939 is clearly dominated by male authors. Balancing corpora in terms of author's gender is not a question about effort or size of the corpus, but about the concept of population and, ultimately, what is considered literature. Achieving a more balanced scenario of past periods will modify profoundly our concept of literature, a virtuous goal for several cultural actors, including researchers and Academia in general. However, these revisions cannot be expected as byproducts of studies of whose main goal has no relation with the author's gender.

#### **3.1.11 Publication of Data**

The publication of the data was important for my project and for the entire CliGS project. A section of the CoNSSA was made available in early phases of the project as a part of the *textbox*, a series of corpora in French, Spanish, Italian, and Portuguese. See Schöch et al. (2019) for a description of the corpora and Calvo Tello, Henny-Krahmer, and Schöch (2018) for several comparative analyses.

Similarly to the textbox, the CoNSSA is made available as a GitHub repository.30 In contrast to the textbox, a section of the texts in the CoNSSA is still under copyright. This restricts the publication of the whole text. For this reason, the CoNSSA has several folders depending on the legal status of the texts. Besides the GitHub repository, the corpus has also been archived in the European platform Zenodo (Nielsen 2013).31As the repository states, the texts themselves are in the public domain. They are provided here with the Public Domain Mark Declaration and can be re-used without restrictions. The TEI XML markup and the metadata are published with a Creative Commons Attribution 4.0 International license CC-BY. The repository also contains a suggestion as to how other researchers can reference the corpus.

For those texts whose authors died at least 80 years ago, three versions of the texts are made available: the master format in TEI XML, the annotated format also in TEI XML, and a simpler file that only contains the *body* element saved as plain text. Around 61% of the CoNSSA (217 novels) is published in this manner.

A recent interdisciplinary article with a focus in Germany has proposed a series of extracted features that can be published even when the texts are still protected by copyright (Schöch et al. 2020). Following this article, I have decided to publish not the entire text but a selection of extracted linguistic features for all the texts that can either be published without restriction or that I digitized myself. These files are saved as tables (CSV files). In these files, each row represents a text, and each column a different feature. Among other features, the researchers can find the frequency in the entire text of tokens (tokenized in different manners), semantic annotation, grammatical annotation, entity information, or TEI tags (see Chapter 4.1 for more details). By that, the most relevant features are made available, covering almost 78% of the entire CoNSSA (279 novels).

One part of the CoNSSA that I am able to publish for the entire corpus is the metadata. Given that the metadata consists of formalized information about the texts annotated by myself, it is not copyright protected. The metadata available also contains the information about the source of the digital

30 https://github.com/cligs/conssa/.

31 https://doi.org/10.5281/zenodo.4674257.

text. This means that even in the cases where I am not able to publish neither the entire text nor extracted features, I am providing the source of the digital text. Reconstructing the remaining 22% of the texts is a feasible task for other researchers.

Besides, as time passes, I plan to publish more and more texts when the moving window of 80 years after the author's date of death allows it. For example, the novels of the following authors will be made available freely as part of the CoNSSA in the coming years: José Diaz Fernández (2022), Miguel Hernández (2023), Ricardo León, and Félix Urabayen (2024). On the one hand, this will slowly increase the proportion of the available CoNSSA as years pass by. On the other hand, this means that, with the current strategy, the entire corpus will be in theory only fully available by the end of the 21st century (Francisco de Ayala died in 2009).

## **3.2 Metadata**

#### **3.2.1 Introduction**

In the previous chapter, I have presented the definition of the corpus. Why should it contain metadata? What kind of metadata is needed to study the subgenre of novels? How can it be collected and validated? What are the general tendencies of metadata between 1880 and 1939? These four questions lead this chapter. Metadata is knowledge about the text, formalized in a way that it is computationally accessible. In that sense, metadata is the way of connecting non-digital research about literature with digital methods. This information can be incorporated into algorithms and tools in different ways: On the one hand, it can be taken as categories in Machine Learning tasks (either to evaluate methods as I will do in Chapter 6.2 or to classify unlabeled cases, as it will be the case in Chapter 3.3). On the other hand, it can be employed as features to classify more complex categories like genre (see Chapter 7.2), or to analyze its relation with other categories or textual features (see Chapter 8 and the Appendix).

For this purpose, I have annotated CoNSSA with numerous kinds of metadata: information about literary genres and subgenres (which sources have assigned which labels of each text), information about literary phenomena like narrator, setting, type of ending, sociological information about the protagonist, etc.

In the final section of this chapter, the corpus will be briefly described based on this metadata, giving a general overview of specific literary phenomena of the novel of this period.

#### **3.2.2 Metadata, Distant Reading, and Hypotheses Testing**

Metadata is at the very core of many textual studies. As Burnard points out "without metadata, the investigator has nothing but disconnected words of unknowable provenance or authenticity" (2004). Even though many researchers or whole areas of research do not adopt the word *metadata*, they employ it as the very basic index. For example, the most standard use case for stylometry, author attribution, needs to assign every text to an author. A subfield of stylometry, stylochronometry, investigates the date of production of the texts, which can be seen as another type of metadata. Library catalogs can be understood as collections of metadata about books (NISO 2004) with a special kind of information: the reference to the place in the library where the book is. Supervised Machine Learning would use metadata, calling it *labels* or only *Y* that represent its classes, while unsupervised methods would apply the available metadata as ground-truth to color and display the results. This metadata can be saved in different ways, one the most basic ones being the name of the file (which is common in stylometry). But other possibilities are also frequent: in simple markup at the beginning of each file, in a structured XML element like in TEI, in a separate table, etc.

Metadata is one of the few elements that can be discussed by non-digital scholars, digital humanists, computational linguists, and computer scientists. However, each of these fields would have different priorities, manners of communicating, and archiving this information. Besides, their interest will differ wildly as far as using this information is concerned. Nonetheless, the different researchers can understand and find it interesting that a novel is written in a third-person voice that is not among the characters of the plot, i.e., a heterodiegetic narrator. The philological interpretation of this fact relating the rest of the literature, the use of this information in clustering or classification tasks, or questions about the optimization of the algorithms would then interest only a section of the researchers mentioned above. That is why I consider metadata the bridge between several fields on the different riverbanks of the digitization.

Actually, metadata can be seen as the computer readable formalization of a cornerstone of Digital Humanities. In 2000, Moretti published "Conjectures on World Literature," in which he coined his influential term of *distant reading*. However, in this article the concept is not related to any digital or computational aim, and there is not a single reference to technology in the entire publication. There, the distance in the reading process is not gained

through computers, but only through the use of secondary literature: "it will become 'second hand': a patchwork of other people's research, *without a single direct textual reading*" (Moretti 2000, 57).

In the next chapters of this book, I will analyze dozens of categories in hundreds of texts using thousands of features. Genres are an optimal phenomenon to be analyzed by approaches like distant reading, macro-analysis, or cultural analytics. One can choose to return to a few excerpts of a couple of novels that show how the analysis is observable in the original text. But by doing so, only a tiny fraction of the paragraphs, novels, categories, and features are being used to corroborate the analysis. There is just too much information to find a solid manner of returning to the original text. We need distance when we analyze genres.

Nonetheless, there are at least two weak points on Moretti's argumentation. First, it is unclear how this process should take place exactly, considering that the different researchers tend to disagree regarding the borders of the analyzed phenomenon, its chronological boundaries, their interest, or their epistemological frame. These differences can be understood as bias from the different sources, and later translate into artifacts in the results. Besides, this way of gaining distance does not foresee the possibility for a researcher to include their own reading and understanding of the text. Even though it is clear that a single researcher is not able to undertake close readings for more than a couple of dozens of texts, they might want to cover specific sections of the corpus that are not analyzed by other sources, or to look closer at very specific characteristics.

In this chapter, I argue for distant reading processes through the encoding of metadata, stating explicitly who provides each piece of information: either myself after reading the text, or other researchers. This could be observed as a productive possible path for interdisciplinary collaboration between several researchers: to analyze through digital techniques the qualitative metadata annotated by literary scholars. Formalizing it in that manner, the same researcher and the community can recover the source of information for each single data point. These metadata represent specific literary interpretations that can be used to explain the results. In other words, the researcher is not obliged anymore to return to a tiny fraction of excerpts of the text when discussing the results; they can remain with the metadata, that summarize specific phenomena of the entire corpus. I will follow this strategy – not to return to the original text – in the rest of this research study. In contrast, I will use the metadata in several ways, such as a mean for evaluating the clusters (Chapter

6.2), encoding it as features for classification tasks (Chapter 7.1), explaining the variance of the results (Chapter 7.2), using them to create a unified digital model of the genre (Chapter 8), and describing empirically each analyzed subgenre (Chapter 8 and the Appendix). In this manner, metadata plays a central role in how my research applies, evaluates, and explains Machine Learning applied to the analysis of subgenre.

In fact, the application of metadata has further implications relating the scientific path for the analysis of literature. First, quality metadata allows for an inductive process of statistical description. These are meant to be exploratory attempts that can either better define one phenomenon or observe its development in historical terms.  But this is not the only case in this work where I will use this literary metadata. Several Chapters like 5.3, 6.1, 7.1, 8, and the Appendix contain similar inductive descriptions based on the qualitative information gathered from several sources.

The second path that metadata opens is related to the critical rationalism, started by Popper: the falsification of hypotheses. This is a central task in this paradigm, and the role of the researcher is, first, to propose a general explanation of the analyzed phenomenon, and formalize it in two or more variables. Then, it is defined what it could represent in a random process (null model) that could also explain the data. Finally, the researcher measures whether the hypothesis can explain the data statistically better than the null model. This last step is called falsification of a hypothesis. Here, the metadata would represent the formalization of the categories of the analyzed phenomenon.

The alternative to a more formalized procedure is the hermeneutic process of interpreting the results of a digital analysis. For that, the researchers take advantage of their knowledge and reading experience. However, this process has several flaws, such as the tendency to observe meaning and connections in unrelated phenomena (apophenia), select only those results that fit well into the hypothesis, and in general over-interpreting results.

For Digital Humanities, there are two possible goals for the hypotheses. Firstly, the researcher might want to learn about the cultural object, in my case, Spanish literature. In this case, the researcher asks whether the gathered data does behave as a specific hypothesis would anticipate, and if it deviates from the random null model. For example, there is an expectation that as the 20th century progresses, the gender differences in literature become smaller with time. This has been partially analyzed in the Section 3.1.10, but it will be further analyzed in the Section 3.2.11, adding the information about the gender of the protagonists. Another hypothesis about the data that will be evaluated in that section is whether the novels of these period lose specificity regarding their references to reality, taking place more often in unspecific periods, locations. This idea is present in works from Literary Studies (García Lara 1986, 209; Gullón 1994, 199; Aubert 2001, 11; Navajas 2008, 18), and it will be analyzed through the formalization of the uncertainty of the metadata of the value *unknown*. The frequency of this value in the entire schema of the metadata will be compared to what is expected by randomness in a statistical regression model.

Besides the data, the researcher can be interested in the deeper understanding of the method: What exactly is the algorithm doing? How is it working? And why? These are essential questions for unsupervised methods such as clustering, in which texts are grouped based on linguistic features. Do these groups relate to any formalized data that was previously encoded? Or are they just randomly sorted? Metadata will be the way of evaluating the results of the clustering methods in Chapter 6.2. But not only unsupervised methods require explanations, there is also a need to a deeper understanding of the results of classification. For that, I will use metadata and other linguistic measures to evaluate a series of hypotheses in Chapter 7.2 about why some subgenres yield consistently higher results than others.

### **3.2.3 Typology of Metadata**

There have been several proposals to outline a typology of metadata. While the NISO differentiates three main types (descriptive, structural, and administrative; 2004), Burnard signalizes four categories (editorial, analytic, descriptive, and administrative; 2004). The difference between both is probably based on the typical use cases the typologies should cover: more general in the case of NISO, while in Burnard's case it is the environment of corpora and editions in TEI. For this corpus, I will consider three types of metadata:

While the first two will be tackled together in the next section, descriptive metadata will be explained separately in Section 3.2.5.

Following the TEI Guidelines, I have encoded directly all the metadata about texts and the collections in the TEI *Header*. This strategy provides me with the opportunity to encode various aspects using a shared semantic frame within the DH community. It also offers the possibility to match up easily specific information through attributes, like *@cert* (about the certainty of a specific information) or *@source* (to refer to a specific origin of data).

Each category and possible value of metadata was defined in a hierarchical taxonomy as an TEI XML document, addressing also which type of information they represent (nominal, ordinal, interval, or binary). In the case of ordinal classes, the names of the possible values were also inserted with their numerical position. For example, five values for different types of endings of the novel, varying from zero (clearly sad) to five (clearly happy). This file has been used for several purposes: On the one hand, to validate the metadata of the whole corpus using a Schematron. On the other hand, to recategorize the fields (for example employing the numerical values of ordinal categories, or transforming nominal information into binary fields) for specific visualizations as the ones at the end of this chapter or to employ them as features (see Chapters 7.1 and 8).

#### **3.2.4 Editorial and Administrative Metadata**

Each text and author have been identified using the complete name, a short name, and the identifiers of the Spanish National Library (BNE) and the Virtual International Authority File (VIAF). In this way, every text and author can not only be tracked within the corpus, but also as a part of other digital collections. About the sources of the text, I have gathered references regarding three different kinds of editions implicated in the genetic process of each text (all of them encoded inside an element *sourceDesc/bibl* with different *@types*):

Other administrative information gathered were the editor (*/titleStmt/principal*), the year of the first version of the text as part of the corpus (*/publicationStmt/date*), a private identifier for the project (*/publication-Stmt/idno[@type="cligs"]*), the publisher (*/publicationStmt/published*), and the legal status of the text (*/publicationStmt/availability/@status*).

The most important adjustments of the editorial process of the corpus (first draft, validation against schema, modernization) have been encoded in the *revisionDesc* as *changes*. Smaller alterations have been recorded as commits in the GitHub repository. Although the information of the commits is not integrated in the TEI file, the potential of Git for versioning and its capacity to give access to the exact status of the file at any given point in time, led the CLiGS project to not duplicate this information as changes in TEI.

#### **3.2.5 Descriptive Metadata**

As I have stated, I consider descriptive metadata those data who represent a categorization of the text considering its internal characteristics. For example, the fact that some sources have labeled a novel as historical novel, or the recognizition of a first-person narrative (autodiegetic narrator). Besides the labels for the subgenres, descriptive metadata have been selected because they are either listed by the history of literature as distinctive features of a subgenre, or their label already reveals the corresponding characteristic. For example, in Section 2.1.8 about erotic novels, different scholars pointed out some typical phenomena of this subgenre, such as middle-class female protagonists in urban contemporary settings. To test if these features really differentiate this subgenre from the rest of the novels, the researcher would need to: select the protagonist, examine their gender and social class and define the temporal and geographical space (for example, Madrid needs to be identified as a city). Finally, the researcher is in the position to compare these descriptive data against other works. Similar examples will be given in the following sections and fully implemented in Chapter 8.

An important aspect for the gathering of the remaining information is a summary of the novel, which has been placed in the element *abstract*. It contains a short summary of the plot of the story and its aim is to give an overview of the entire text. These have been either written by myself after reading the novel, or they have been typed or copied from external sources, such as the MdLE, Wikipedia, or ePubLibre. An explicitly reference to these

sources has been kept in an attribute. Thus, the distant reading process can be traced back to the source of interpretation.

Most of the descriptive information has been structured as discrete information into different elements. Many of these categories do not have a specific element in TEI yet, or the possibilities are clearly insufficient. A clear example is the information of the genre. The current TEI Guidelines give different options, one being the attribute *@n* (*/profileDesc/textDesc/@n*). This *@n* does not have any specific semantics (its normal use case is a number, but it can contain other elements as well). As I will explain in the following sections, the information about genre that I wanted to collect was much more complex than a single value in an attribute. That is why the members of CLiGS have decided to use the element *keywords* in *textClass*. Each type of metadata can be encoded in a separate term element, defining its class with the *@type* attribute. The structure of the metadata is controlled by tokens separated by a dot, making even similar metadata easy to understand and differentiate, like *author.gender* and *text.characters.protagonist.gender*.The metadata values are validated against a Schematron.

In the corpus, every keyword element has been assigned with a *@cert* attribute with three possible values:

Depending on the analysis that I undertake, these values of certainty can be used to filter them, for example, ignoring the data from novels with a low level of certainty. In CoNSSA-canon, all texts have either medium or high certainty. Regardless of the general certainty of the keywords, any term can also have an individual *@cert* attribute, matching up exactly to specific information its degree of certainty.

The metadata about setting, place, and protagonist of the text can be unknown for different reasons: Either the abstract does not contain the data, or the information is not available at all in the whole text. To illustrate the last scenario, I would like to explain one case: in the novel *Aventuras del submarino alemán U*, by Ricardo Baroja, the autodiegetic narrator does not mention his name. The first case of uncertainty ("I do not know it, but probably the information is in the text") has been simply assigned with a question mark; the

second case ("I have read the text and I am certain that the information is not there") has been described as *unknown*. A hypothesis about these types of values will be analyzed more closely in the final section of 3.2.11. Most metadata classes also foresee an option called *other*, in cases where the typical values are not acceptable: for example, when assigning gender to protagonists that are objects, like the protagonist carriage of *Memorias de un vagón de ferrocarril* by Zamacois.

### **3.2.6 Author's Metadata**

Apart from his or her name, general information such as gender, year of birth and death, etc. has been gathered about each author. After collecting the BNE identifiers, it has also been possible to obtain further metadata such as the place of birth and death of each author from Wikidata (through Linked Open Data).

Two types of information about the relation between author and text have been collected:

## **3.2.7 Text and Genre Metadata**

As mentioned before, genre and subgenre categories are more complex than others like authorship or periodization. It is absurd to dispute whether a year existed or not, and there are few cases in which the existence of a person is discussed. In contrast, the discussion of the status of many genres and subgenres is commonplace. While the boundaries of people and years are obvious, it is debatable whether subgenres have boundaries at all. Although a text can technically either belong to several authors (for example in case of multi-authorial texts) or to different years,1 these are rather exceptional cases. Contrary to that, texts blending various subgenres are rather the norm (see Sections 2.3.3 for a discussion about the macro-models of genres).

The method of encoding information of genre has to be able to capture this complexity. For that, I have defined numerous levels (genre, subgenre) and sources (cover, history of literature, libraries, printing houses), some of them hierarchically:

1 For example, an author who started writing a text when she or he was young. She or he finished the first version after a couple of years, she or he published the work with changes several years later and made a new version of it at the end of her or his life.

2 Mentioned as prose texts by, for example, Ródenas de Moya (2000, 49).

• *Own annotation about subgenres*: Labels from a controlled vocabulary of 27 different novel subgenres. These labels were assigned by me after collecting the abstract, encoding the metadata, and reading which labels the other sources used.

The different labels of the various sources will be closely analyzed in Chapters 5.1, 5.2, and 5.3.

#### **3.2.8 Place and Period of the Action**

Many subgenre-labels contain information about the place of the action of the plot. There are numerous types of geographical references: *novela americana* (reference to the continent), *novela regional* (to a rural setting), *novela madrileñista* (to the city or town), *novela cosmopolita* (to the size of the place), *novelas del mar* (to the sea, in contrast to the land). These multiple facets of the setting have been encoded using different categories of metadata. Many of these are implied: If the action takes places in Madrid, it means that it also takes place in Spain, which is in Europe, and it means that the sea is far away. Nevertheless, I have explicitly encoded all the fields to use this information in various scenarios.

To assign a value to each metadata, at least half of the novel has to correspond with the value. Otherwise, it was assigned as *mixed.* This specific value is especially interesting because it indirectly represents travel; depending on the geographical level of this value, it can point out travel within Spain (for example *La voluntad* by Azorín), Europe (*El mundo es ansí* by Baroja) or the world (*Genio y figura…* by Valera). These will be observed closely in the descriptions of the subgenres of the Appendix.

Along with the place, information about the period (point and total span) has been gathered. Here is the complete list of the categories:

3 Maya is fictional, but placed somewhere in Africa; Iberina, in Spain; Liliput, in Australia; while Surlandia is completely out of this Earth.

4 Although the basic unit for the measure of chronological span is days, I always use the next lowest unit of time. That means that, if the action lasts about one month, I encoded 30 days; if it lasts about one year, I have assigned 365 days. Therefore, values such as 35 or 380 are not possible unless the texts specify this as the exact chronological span.

## **3.2.9 Protagonist Metadata**

Another type of literary information with a strong resemblance in the labels is the protagonist. Some subgenres are related to different types of information about the most important character in the novel, like her or his social level (*picaresca, social*), profession (*de guerra, policíaca, taurina*), or the age at the beginning of the action (*picaresca, de educación*). Two other basic pieces of information about protagonists have been gathered: their gender and their name (to document which character has been chosen, particularly pertinent in novels in which this decision is unclear). The specific fields are:

5 I would have encoded other categories such as transgender, but these have been not found in this corpus.

6 This information is affected by the type of setting. A wealthy character in the capital tends to be much wealthier than one from a village. This aspect is even more obvious for some lords from rural areas that have economic problems despite their social position, like in *El mayorazgo de Labraz* by Baroja or *Sonata de invierno* by Valle-Inclán. They are still marked as high social class, because they represent the highest status in comparison to the rest of the characters of the novel.

has been used.7 The age of the protagonist at the end of the plot can then be calculated adding this value to the time span of the action, explained in the previous section.

### **3.2.10 Other Literary Metadata**

Finally, several literary fields related in different ways to genre have also been collected: data about the narrator, whether or not the action takes place in a realistic world, if the novel has a happy ending, and the canonization of the text measured in different ways.

7 Some novels include a flashback to the childhood of the protagonist and represent this period over several chapters, like in *Aventuras, inventos y mixtificaciones de Silvestre Paradox* by Baroja. For shorter flashbacks, the reference to childhood has been ignored.

8 One interesting discussion is what should be considered realistic: Does a novel still take place in a realistic world if angels or the Virgin Mary affect some situations? Does it depend on the encoder's beliefs? What if instead of angels, the creatures are vampires or aliens? The definition of what is realistic cannot be universally defined without cultural bias. Lexicography has already gone through similar discussions about how the cultural and personal values might reflect definitions (Swanepoel 2011; Rodríguez 2013; Lara 2016). I have decided to consider realistic what most of Spanish society of the 19th century could consider realistic, accepting the Christian supernatural beings.

of a new period of their lives (like *La conquista del reino de Maya* by Ganivet, or *Volvoreta* by Fernández Flórez).

• *Quality in history of literature*: Along with the number of pages used in the MdLE, any reference to the quality of the novel in the manual has also been gathered, collected as three possible ordinal values: low, medium, and high.9

#### **3.2.11 Description of the Metadata of CoNSSA**

In this final section, my goal is to describe the corpus in terms of metadata. By doing so, I want to achieve three different goals: Firstly, I show how already existing knowledge in form of digital metadata (coming from summaries or personal readings) can uncover broader pictures about literature. In other words, metadata is the first step to run distant reading analyses that are not explorations of uncertain tools, but inductive statistical descriptions of literary features. Secondly, I describe the general tendencies of novels of this period. These general patterns of the entire genre can be used to compare specific characteristics of some subgenres: Are urban or contemporary settings specific to the erotic novel, or are they just the most common values in all the novels of that time, regardless the subgenre? Finally, I explore the most noticeable changes of specific literary phenomena over time: For example, are sad endings more common at the beginning of this period and therefore more typical in subgenres of the 19th century like in the realist or naturalist novels?

The first step is then to examine the common values in all novels of the corpus. For that purpose, I apply central tendencies from Descriptive Statistics such as median and mode. Besides, I report the proportion of novels that satisfy these value in CoNSSA.  More specifically, in a big city (47%) like Madrid (29%), in contemporary times (94%) of a realistic world (89%) that does not resemble the author's life (72%). The action lasts around

9 It is interesting to see that, typically, the information given was referencing the quality of the work within the author's production ("one of his best works") and not universally ("a good novel") or within the period ("a rather bad novel for this decade").

one year (30%) and it clearly has a sad ending (50%). Although the proportions and deviation change, central tendencies remain similar in both CoNSSA and CoNSSA-canon.

These values are calculated independently from each other, and actually in both versions of the corpus there is one single novel which fulfills all these criteria: *Tristán o el pesimismo* by Valdés. This novel can be seen as the one whose literary characteristics are most similar to the rest; in other words, the least surprising work in terms of its metadata. Deleting the two most strict criteria (the duration of the action and the specific place), 17 other novels fulfill the rest of the criteria, with works by Alarcón, Baroja, Blasco, Francisco de Ayala, Jarnés, Lanza, León, Miró, Munilla, Pemán, Unamuno, and Valle-Inclán (details in the Jupyter Notebook). As a summary, a short empirical definition of the novel of this period can affirm that the story typically takes places in a realistic contemporary Spain, in third person, written and starred by men, with a sad ending.

After looking at the general picture of the novel, I continue investigating the distribution and the development of specific literary metadata during this sixty-year period. As I have shown in Section 3.1.5, some subgenres were rather published in the last decades of the 19th century (naturalist, realist novels), others in the 1900s and 1910s (erotic novel), and a third group in the 1920s and 1930s (social, historical, and comedy novels). That is why the general development of literary patterns are not only interesting per se, they also can shed light on internal characteristics of subgenres.

One field that shows a clear chronological pattern is the information of whether the text resembles the author's life.10 As described above, the majority of works does not, with 73% of the cases. The number of novels that are somehow autobiographical increases over the analyzed period (Figure 27): While 88% of the texts in the decade of 1880 were clearly not autobiographical, this amount drops to 58% by the 1930s. Similar tendencies are observable in both versions of the corpora, with an even stronger tendency in the canonized one, both reaching high statistical significance when analyzed through linear regression (p-values < 0.001 in both versions).

This tendency is one of the most surprising and solid results of the description and a clear example of the potential of distant reading through metadata of the *silver age*. As Figure 27 shows, autobiographical traces in the

10 Ródenas de Moya pointed out that self-reference (*autorreferencia*) was a tendency in modern art, although this is not undisputed (1998, 93–94).

#### *Figure 27: Autobiographical representation in the corpus over time*