*Article* **Expert–Novice Negotiation within Learning Opportunities in Online Intercultural Interactions**

#### **Amira Benabdelkader**

Modern Languages and Linguistics, University of Southampton, Southampton SO17 1BF, UK<EMAIL_ADDRESS>
Received: 16 January 2019; Accepted: 18 February 2019; Published: 21 February 2019

**Abstract:** Computer-mediated communication (CMC) and e-communication tools have introduced new pedagogical tools and activities that contribute to the development of language learners' academic, multilingual, and intercultural skills and competences. Moreover, CMC has reinforced communication and collaboration between individuals and educational institutions through projects of intercultural language exchanges (ILE). Most of these exchanges idealise 'nativeness', and assert the L1 speaker as an expert 'by default'. These models of ILE believe that the incorporation of a L1S is key to the creation of learning opportunities. This paper contests this belief. The one-to-one online video conversations took place on Skype between language learners of English and/or French over a period of four months. The dyads comprise the following speakers' constellations: a L1S of French with a L1S of English, and a L1S of English with an Algerian (L2/LF of French and English). To assure equity in the use of languages, I scheduled two sessions every week, one in English and the second in French. This paper investigates the expert/novice dichotomy and how it is negotiated in the learning opportunities they have created. It also casts light on the speakers' communicative strategies and linguaculture(s) included in overcoming intercultural misunderstanding and miscommunication when using or not using their L1, French and/or English. These intercultural interactions have uncovered that the novice–expert roles alternate between the speakers despite the language of communication and their L1s. The interactants used several strategies and channels, namely pragmatic strategies such as repetition, nonverbal cues to ask for clarification and signal intercultural misunderstandings, translanguaging and their multilingual repertoires in order to construct meaning, achieve their communicative goals or in case of the lack of linguistic resources.

**Keywords:** novice–expert identities; online video conversations; synchronous communication; intercultural interactions; English; French

#### **1. Introduction**

In this ever-globalised world, foreign language learning is no longer confined to educational institutions since electronic communication has been facilitated through the technologies that offer easy access to other language speakers and cultures (Kramsch and Thorne 2002). In the field of English language learning (ELL) and teaching (ELT), there has been a considerable emphasis on the inclusion of cultural content about the self and the other in education (Risager 2007). Internet, which has become in the last few decades a virtual space for socialisation, learning, teaching, entertaining, etc., has been considered as a fertile area for researchers to investigate online behaviour(s), and/or to compare between the human behavior in real life and virtual spaces. The second main reason is thus studying internet users' or participants' online behaviours (Walther 1999). Given the global mobility that has been promoted by online means of communication, the behaviours that are under the loop in this study are the communicative ones. In this respect, (Seidlhofer 2011) underscores that e-communication tools "have accelerated and forced changes in the nature of *communication*: the *media* now available have changed the *modes* of use". We have to acknowledge that these tools have 'freed' communication from spatial and temporal frames, and also emphasised the use of language as vehicular of meaning to achieve interlocutors' communicative goals.

This virtuality in communication does not hinder face-to-face interactions because it allows electronically simulated face-to-face conversations. In other words, communication technologies mediate video interactions. Skype, for instance, is one of the many platforms that allow electronic face-to-face interaction. Kappas and Krämer point out "[b]y means of [Skype] and a small webcam, as integrated in conventional PCs or laptops, one can conduct video-mediated conversations with people all over the world who use the same technology" (Kappas and Krämer 2011, p. 3). Furthermore, computer-mediated communication (CMC) does not only offer a platform for communication but also contributes to it. The ability to arrange online face-to-face conversations allows the nonverbal language to contribute to communication as it normally does in face-to-face communication. Moreover, the intercultural dimension is much emphasized because the CMC affordances contest the traditional known challenges, namely geographical locations and time zones. In other words, the cultural aspect could have its place through the inclusion of speakers from different linguacultural backgrounds.

Many research findings have proven the efficiency of online international partnerships in engaging students in collaborative projects and intercultural communication through the use of English and a few other languages (Guth et al. 2012; Kern 2015). Those partnerships target mainly L1 speakers and ignore the inclusion of other populations who could participate with their foreign languages. That is, the expert role is already assigned to the partners before even undertaking the exchange. As a result, this undervalues the abilities and competences of other partners and promotes the existing unbalances, especially those related to imperialistic powers.

Furthermore, research that has been undertaken on CMC focalizes mostly on the affordances of the online platforms while little has looked at the dynamics of learning and communication through those platforms (Liddicoat and Tudini 2013). For this, this paper aims at bringing into light how the affordances of CMC intertwine with the communicative dynamism to shape online communication. To achieve this, L1 and non-L1 speakers of English and French are paired and engaged in online video conversations. The asymmetries related to languages status, proficiency and/or deficiency will be scrutinized in the light of the sociocultural theory. The motives behind undertaking such a study are mainly to highlight the potential opportunities available for online exchanges outside the boundaries of L1 speakers in order to be more inclusive of foreign language(s) learners all over the world.

#### **2. Study Design and Corpus**

This study took place in an online setting where language learners, who have different linguacultural backgrounds, are involved in synchronous conversations. These intercultural conversations were held on Skype to allow more than two interactants, myself (the researcher) and two speakers, to be involved in synchronous audiovisual conversations. Ideally, the conversations took place twice a week, one in English and the second in French. There were eight culture-related topics:

At first glance, such a design can take our attention to the use of a task-based approach in language teaching and learning. It should be recognized that there exist synergies between computer/ technology-assisted language learning and a task-based approach (Thomas and Reinders 2010). In order to have a better understanding about the nature of this study, it is of great importance to bring into light the definition of 'task'. According to Ellis (2003),

A task is a workplan that requires learners to process language pragmatically in order to achieve an outcome that can be evaluated in terms of whether the correct or appropriate propositional content has been conveyed. To this end, it requires them to give primary attention to meaning and to make use of their own linguistic resources, although the design of the task may predispose them to choose particular forms. A task is intended to result in language use that bears a resemblance, direct or indirect, to the way language is used in the real world. Like other language activities, a task can engage productive or receptive, and oral or written skills, and also various cognitive processes. (p. 16)

Undoubtedly, there are few overlaps between this study's design and Ellis' definition of task in the sense that these language(s) learners/users get involved in this study through the use of their linguistic resources and repertoires as well as inherited and co-constructed meanings. However, evaluation is neither planned nor intended. That is, the aim of this study is to tease out the (intercultural) communicative tendencies and strategies of language(s) learner/users, especially in instances of (intercultural) non- or misunderstandings. Moreover, it should be noted that all the guidelines provided prior to the conversation do contain a clear statement whereby the interactions are meant to be as natural as possible and any deviation from the pre-set topics would be accepted. The pre-set topics along with their guidelines work as stimuli and (ready-made) initiations for the interaction, and for the speakers to return to in case they run out of ideas and topics for discussion. To make it short, I can argue that this study has been inspired by a task-based approach but it does not conform to it.

During the interactions, the researcher's role is to start the call, give the guidelines about the topic of the conversation, and then close the conversation. The role I play then is to record, observe and take notes. When the two speakers express their readiness to start the conversation, the researcher turns off the mic and cam to avoid any interruption or interference (see Figure 1).

**Figure 1.** Data collection process (video-recording the conversations).

The pairs involved in the study are presented in Table 1. The latter informs about the linguistic and cultural backgrounds of both speakers in each pair.

After the transcription process, the conversations content was coded using NVivo. The excerpts below are taken from the code "learning opportunities". The extracts below are taken from English and French interactions of the pairs shown in Table 1. In order to make them eligible for the reader, the French extracts contain interlanguage translation with the glosses that correspond to the scope of this paper. The incorporation of any language(s) other than the language of interaction is italicised, so that it can be distinguished.

**Table 1.** Participants' linguacultural backgrounds.

Note: Italics denoted the languages under study.

#### **3. Hesitating Novice and Provoked Expert**

In this corpus, hesitation represents one communicative strategy through which the interactant expresses their incertitude about the correctness and/or appropriateness of linguistic items and forms, and also about their intelligibility. In most cases, the former use triggers a learning opportunity, which is the main focus of this paper. That is, the incertitude expressed by the speaker is often understood by the listener as a call for correction or confirmation. The interaction cannot move forward, at least in the same point, until this incertitude is faded away. Admittedly, because hesitation signals the speaker's "inability to move into action after making a decision (MacIntyre and Doucette 2010, p. 164), the contribution of the listener here is of great importance in making the speaker relaxed and reassured, and also in protecting the communication from any potential breakdown.

#### EXTRACT 1

1. Sonia: yeah sure <EYEBROWS UP> erm my brothers never ask my mother or never tell us where they go and how they spend the day and with whom erm the parents or the mother generally is very au-tho-ri-ta-ry <SPELLING IT SLOWLY IN ENGLISH> like *autoritaire* <PRONUNCED IN FRENCH> authoritary <NOSE UP>

To give it a context, this extract is taken from an interactional event in an English conversation where Sonia and Gracie are discussing how social conventions dictate to females certain behaviours, and how parents in turn impose them in case daughters intentionally or unintentionally want to violate them. In turn 1, Sonia shows hesitation by articulating the word "authoritary" slowly, i.e., she almost gave a syllabification to the word. She appears not to be convinced about the correctness of the word when she browses her French repertoire to come up with *autoritaire*. The use of *autoritaire* here may have several indications. First, she is initiating a self-repair by using a shared repertoire between her and Gracie. By this, there is an uncertainty in that the word "authoritary" could have conveyed the target meaning. Second, "like *autoritaire*" may stand as a signal of linguistic weakness and a call for assistance or correction. According to both interpretations, because it is followed by "authoritative", a nonverbal

cue that indicates uncertainty, *autoritaire* has not functioned as a replacement of an equivalent English word but rather as a way to reach out the latter.

Sonia deploys hesitation followed by code-switching then hesitation. Those strategies signal the need of an expert to rescue a novice. That is, she categorises herself as a novice and provokes the role of expert to Gracie. In turn 2, after showing a slight hesitation, Gracie accepts to be an expert by correcting the token. After that, the novice role has not been abandoned. In other words, Sonia's next turn shows a need for another assistance. Repeating "authoritative" with a tone of uncertainty does indicate that she is taking a novice orientation, and emphasising the novice–expert dichotomy between her and Gracie. Likewise, Gracie does not resist or hesitate in answering Sonia's need that is correction. As a result, the interaction in this extract conforms to the self-initiated other-repair, as Schegloff et al. (1977) put it, which then ends by turn 5 when Sonia returns to completing her idea in turn 1.

Hesitations and code-switching are deployed to reflect an uncertainty attitude vis-à-vis the correctness of the linguistic token "authoritary". The uncertainty attitude creates an asymmetry regarding Sonia's use of English in this interaction, which makes her not only proclaiming a novice role but also the one managing the novice–expert orientations by taking one role and provoking another. In other words, the asymmetry is not intended nor negotiated with Gracie, yet triggered by a deficiency in Sonia's repertoire of English. The situation provokes Gracie to respond to Sonia's attitude by taking over the role of an expert. The process of being a hesitating novice and provoked expert produces a self-initiated other-repair that hinders any disruptions to achieving the speakers' communicative ends.

#### EXTRACT 2

This excerpt, which is taken from a French conversation, also shows how the novice role is taken while the expert is provoked. Louisa deploys some nonverbal cues (see turn 1 the cues between <>) and the token "scandinavi" to indicate the inability to find the right suffix or to form the word *scandinave*. To add, "scandinavi" could mirror how Louisa's English and French repertoires intermingle and cause such a confusion. The eye contact by which she ends her turn also serves as a signal to give away the floor to Shayma to complete the word or provide a correction. In this respect, eye contact represents a sharp edge as it distinguishes between the speaker and listener roles. Indeed, Shayma takes a double role, speaker and expert. Each of the roles are fulfilled once a response to Louisa's need has been delivered. It is *scandinave* that indicates that Shayma has accepted being a speaker with expert orientation.

Turns 3 and 4 illustrate the same asymmetry expressed in the previous turns. However, Louisa in turn 3 utilises communicative tokens different from the ones used in turn 1. The inclusion of the English repertoire is much explicit. The use of code-switching by the inclusion of "Norway" and "Norwegian" reflects how the repertoire of English and French interconnect and interact. The way in which those items are uttered hold hesitation, a call for assistance, or more precisely for translation, and/or a confirmation for intelligibility. Shayma ignores the first call and only responds to the second. That is, she does only give the equivalent for the lastly uttered word, "Norwegian". Nonetheless, this does not deprive her from the expert role. Similarly to turn 3, turn 5 starts by *oui*, a confirmation from Louisa that Shayma's response is the one needed.

The sequence above is made up of two self-initiated other-repairs. In the two cases where Louisa signals a problem through first hesitation, then hesitation and code-switching, there is a repair offered, and hence the problem resolved. Although the dynamics of this repair differ from that in extract 1, the deployed strategies and how the expert–novice dichotomy has been invoked remain alike. In sum, extracts 1 and 2 show how the novice is an unescapable situational role while the expert is an interactionally-responsive and provoked role.

#### **4. Implied Expert–Novice Orientations**

The asymmetry may sometimes be indirect without any mechanisms, strategies, or machinery. In this situation, the expert–novice orientations emerge implicitly without being preceded or followed by any signals. In some cases, like the one illustrated below, not all speakers should cooperate or be incorporated, though they are implicitly involved.

#### EXTRACT 3

Even though there is no potential nonunderstanding, misunderstanding, unintelligibility or any other risk for communication breakdown, novice–expert orientations still take place in extract 3, an interactional event in a French conversation. In Louisa's two first turns, there is a repetition of the sentence *tu as beaucoup de faire* ("you have a lot to do"). The repetition might be used to encourage Shayma to give further information about her readiness for her trip to Italy, or to seek feedback on the correctness of this phrase. Shayma's utterance, turn 4, takes a clarifying account without referring to nor correcting Louisa's sentence. Instead, she inserts *j'ai plein de trucs à faire* to emphasise how she is pressed for time.

On the other hand and according to Louisa's turn 5, the novice and expert roles are still allocated to Louisa–Shayma respectively. If we are to compare the syntactic structures of Louisa's sentence in turns 1 and 3 with the one in turn 5, the differences would be dismissing the phrase (Adj+Preposition) *beaucoup de*, introducing the plural noun *choses* preceded by a plural indefinite article *des*, and adding the preposition *à*.

Louisa's adaptation of Shayma's sentence *j'ai plein de trucs à faire* to *tu as des choses à faire* idealises Shayma's use of French. That is, the identity of Shayma as a L1 speaker is highlighted by being a model whose use of the language, French in this case, should be imitated.

The change conducted on Louisa's sentence (Table 2) assigns to her a novice orientation while conceiving Shayma as a model attributes to the latter an expert orientation. It appears that Louisa chooses such an implicit division of roles based on the fact that Shayma is a L1 speaker of French. Perceiving L1 speakers as a model implies giving them the status of an expert and considering non-L1s as novices. This operation, including the change or adaptation of form, does not have an apparent linguistic or didactic orientation. None of the turns in extract 3 hold signals to any linguistic weakness or to seeking assistance and correction. Moreover, even after Louisa's adjustment, no one has given comment on the linguistic form. Instead, the conversation goes on (see turn 6) focusing on the messages' content rather than form, i.e., grammatical accuracy and correctness.

**Table 2.** Form/syntactic adaptation.

In extracts 1 and 2, the L1Ss accept to take up the role of expert. As mentioned above, their responses to the hesitating novice show their acceptance to be identified as experts. On the other hand, as shown in extract 3, the expert role can be implied instead of being triggered. Besides, the novice is implicitly embraced. With no strategy indicating the need for linguistic assistance or correction, the novice, Louisa, here deploys an aspirational learner role. Despite that the interaction is not explicitly shaped to take the form of a learning environment, she ends up adjusting her utterance through imitating Shayma's use without interrupting the interaction flow or giving it a mere didactic orientation.

#### **5. Assigned Expert–Novice Roles**

L1 speakers may cherish idealisation, which can be unveiled through explicit and direct communicative practices and strategies. In most of the cases, this idealisation is performed when a non-L1 interacts as a novice learner. In this context, this novice generally gives up the language user role and identifies as a language learner. Ideally, the learner addresses the L1 speaker as a language teacher instead of a language user.

#### EXTRACT 4

Extract 4 is taken from an English conversation. In turn 2, Elise identifies herself as a novice through a direct question querying the spelling of the linguistic token "gatherings" and a statement to highlight a lack of knowledge "I don't know". Tania hesitates on how to answer Elise then decides to send her the word in a text message on Skype. After receiving it, Elise asserts that the word has not been introduced to her English repertoire. This has led Tania to give examples where gatherings usually take place. In spite of this, Elise re-asserts that the word is new. This process is very straightforward and smooth. On one hand, Elise does not show any hesitation to express her novice-ness. Tania, on the other hand, takes up the expert role assigned to her.

#### EXTRACT 5

In extract 5, emerged in a French interaction, the mispronunciation of the word *vielle*/vjεj/ in turn 1 causes a nonunderstanding signaled in turn 2, which alters the interaction nature being narrative (Tania talking about her trip to a Belgian city) into a didactic nature focusing on the meaning, correctness, and appropriateness of the use of *veille*, which literally means eve. The adjustments that Tania made are *vieux*, a masculine adjective, and "old", the equivalent in English. These linguistic tokens are retrieved from the French and English repertoires respectively, and share the same meaning. As a result, this forms a four-turn other-initiated self-repair, that is signaled by Elise and repaired by Tania.

As the interaction goes on, Tania does not seem to get the assistance she aimed for when she provided the masculine form of the adjective. The incompatibility between the gender of *vieux* and *ville* leads to violating adjective–noun agreement, which she clearly wants to avoid in turn 5. Tania's strategy to do so consists of incorporating Elise in a sentence completion task. The only item that the sentence lacks is the adjective *veille*. Although turns 1 to 4 clearly show that Tania is taking the role of a novice who is looking for the correct feminine from of the adjective *vieux*, the task given to Elise may indicate that the roles are reversed. To explain, the task takes a form of scaffolding through which teachers mediate their students to encourage them to find the right answer(s).

Consequently, Tania is adopting an expert practice in order to stimulate the 'real' expert to finalise the other-initiated (turn 2) other-repair (turn 6). In the end, one can argue that Tania's strategies are deemed to be successful, especially since she adequately triggered two repairs. The first repair (turns 1 to 4) that is other-initiated self-repair contributes to the construction of the other-initiated other-repair. In addition, the process of claiming a novice identity and uptaking the expert runs smoothly from one turn to another without causing miscommunication.

What is common between extract 4 and 5 is that the asymmetry has been distributed in a systematic way. That is, the non-L1 speaker of either of the languages, French and English, assigns to herself a novice identity by which she shows lack of linguistic knowledge. By this, the L1 performs a complementary role, yet may be also seen superior to that of a novice. Being the more knowledgeable, language-wise, assigns to her the expert role. Those roles imply unequal transformation of the roles of languages' users; one becomes the 'imperfect' whereas the other becomes the model.

#### **6. The Outsider Expert**

The imperfect-model may be contested when speakers perceive themselves as equal contributors to the interaction and users of language(s). Any difficulty, weakness, confusion, or uncertainty can be addressed to someone and/or 'something' not involved in the interaction, an outsider, instead of the expert 'by default'.

#### EXTRACT 6

In turn 1, Elise makes use of her French repertoire to find the word "parade". Although uttered in French, it did not cause any confusion for Tania as turn 2 shows. Despite that, Elise prefers to look up the equivalent in English. This does not allow the conversation to carry on in its intended direction because being immersed in the online dictionary results in not being able to give an answer to Tania's query. Turn 3 pushes Tania to use her 'default' identity, and thus provide the searched token. Afterwards, Elise confirms Tania's answer as it apparently matches the one given by the dictionary.

According to Elise's practice of forwarding her query to the online dictionary, the latter here is undoubtedly the expert while both speakers, Tania and Elise, are novices. However, Tania challenges and also negotiates this distribution of role by spontaneously performing her 'default' identity of expert. To add, another negotiation takes place in turn 5. The act of confirming the answer of Tania does categorise Tania as a novice, or an expert with an inferior position in the presence of the online dictionary. The whole interaction illustrated in extract 6 portrays how the affordances of communication technologies may lead to rejecting the ideology of idealising L1 speakers. That is, and in this particular situation, the online dictionary plays the role of a mediator that facilitates the novice's learning of a 'new' linguistic token.

#### EXTRACT 7

In extract 7, Elise is trying to accommodate Tania's communicative needs by explaining the French word *gêne* through English, Tania's L1. Given that this interaction is in French and her linguacultural background (Table 1), Elise is being the expert here; the one who knows the meaning of *gene*. However, "I try" in turn 1 does show that the dictionary has a superior expert position. Moreover, switching to English and not being able to pronounce "awkwardness" have promoted Tania's position from novice to an in-between position, i.e., neither a mere novice nor a mere expert. In a way, her position alternates as the language changes. Nevertheless, the alternation does deprive the dictionary from being an 'outside' expert.

#### EXTRACT 8

The first four turns are similar to extracts 4 and 5. In this event extracted from a French interaction, Gracie plays the role of novice and Sonia the role of Expert. The following turns, especially turns 5 and 7, create a sense of doubt vis-à-vis the appropriateness of the use of *séries* for shops. This doubt affects Sonia's confidence, and also signals the need to negotiate or repair the meaning of *chain*. Sonia's response in turn 6 is firmer than that in turn 8. That is, in turn 6, she accompanies her verbal affirmation with the nonverbal cues, namely nodding her head and raising up her eyebrows. In turn 8, however, this affirmation was not endorsed by any nonverbal signal. After a few turns, Sonia goes back to translating the word chain through an online dictionary. At last, she makes up her answer and adopts the one provided in the dictionary. In such a case, she gives up her assigned expert role to the dictionary. This process of repair ends by Gracie appraising Sonia's effort to finding the French equivalent to *chain*. By going through the process of trigger, signal, response, then reaction, this interactional event mirrors a meaning negotiation instance (Doughty 2000; Nakahama et al. 2001).

The gender of the word *offre* creates confusion for Sonia, the 'default' expert in this French interaction. Gracie, who is the novice, expresses her relief in that even the expert could get confused and commit mistakes like her (turn 4). Sonia asserts in turn 3 and 5 that nouns' genders have always been a problem for her. This declaration then leads Gracie to think of another expert to interfere in the interaction and resolve the problem. She hence suggests Anna, the researcher. At the same time, Sonia chooses the assistance of the online dictionary to finally find out that the word *offre* is feminine and should be preceded by a feminine indefinite article *une*.

Gracie's first reaction to Sonia's confusion indicates that she perceives her as a model. The idealisation attributed to Sonia has been declined, yet transmitted to the researcher. According to Gracie, the researcher enjoys an expert position higher than that of her peer, Sonia. The latter, on the other hand and as being the expert in this interaction, considers the online dictionary as a legitimate alternative.

#### EXTRACT 9

#### **7. Discussion and Conclusions**

The online video interactions presented above provide many uses of the novice–expert asymmetry within the dyad. The several uses of the novice–expert dichotomy and the shifts in these roles and identities hold a great indication to the non-fixedness of the roles. This in turn contests the fixedness attributed to the presumptions and perceptions in regards to the roles assigned L1-nonL1, also labelled as "NS-NNS". The fixedness related to those dichotomies lies in considering the non-L1 as an imperfect user, or eternal learner of the language, whereas the L1 as an idealized user. This controversial view claims that the latter always represents a model for the former. That is, the learner or the novice should dedicate every effort in their learning process for the purpose of reaching the L1, i.e., the expert, use of the language. As Hua (2016) put it, the learner is thus regarded as "an unfinished product".

Admittedly, the interactants have brought into these interactions the expert–novice identities. Bakhtin's heteroglossia explicates how the language users shift to learner–teacher roles. The non-L1 interlocutors translate the EFL inherited "unfinished product" into assigning themselves novices' roles. Similarly, L1 speakers are given the idealized status of language users, i.e., the model of the "unfinished product". However, the aforementioned negotiations and shifts in assigning and accepting these identities go in line with the sociocultural approaches that "deemphasize the stability of systems and the presumptions of consistency across contexts, time periods, individuals, and communities including predefined characteristics such as 'expert' and 'novice'" (Thorne and Hellermann 2015, p. 320). Unlike the findings of Liddicoat and Tudini (2013), it is less common in this study that the L1 speaker claims the 'default' expert identity. Rather, it is more common the novice who triggers this

identity through seeking assistance. The dyads show great awareness regarding the non-fixedness of those roles. That is, they are negotiated, claimed, accepted, or denied.

Furthermore, this asymmetry, whether assigned, provoked, imposed, negotiated, or denied, does not hinder creating learning opportunities that emerge as ultimate interactional outcomes. In other words, in all the extract discussed above, at least a member of the dyad ends up acquiring a new information, grammatical form and/or a linguistic item. It can then be argued that these online video conversations do contribute to the speakers' zone of proximate development (Vygotsky 1978). This mechanism acknowledges that the learner needs an expert who teaches or guides them to acquire/learn a certain amount of sociocultural knowledge by which they can partially or totally interact independently, i.e., they develop from learners to experts. In the context of this study, the less knowledgeable, the novice, does rely whether on the peer or the internet affordances to overcome the weakness and acquire new knowledge. To put it differently, this process contributes to the construction of what Bourdieu refers to as linguistic capital (Bourdieu 1992). The participants' linguacultures or languages proficiency and status do contribute, but not hinder, to the deployment of expert–novice identities in order to achieve the learning opportunity, repair or meaning negotiation. Importantly, the multilingual repertoires of the participants enrich their interactions and encourage understanding and the success in attaining the speakers' communicative purposes. At last, and given that they are treated as situational and non-fixed, being novice or expert does not create any kind of sensitivity, inferior or superior attitudes.

**Funding:** This research has been funded by the Algerian government.

**Acknowledgments:** I would like to thank my supervisor Dr Jaine Beswick for her valuable support and guidance and the *Languages* reviewers and editors for their useful comments. I also would like to express my gratitude to my participants who massively contributed with their passion, devotion and commitment to my research project.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

Bourdieu, Pierre. 1992. *Language and Symbolic Power*. Paris: Arthème Fayard.

*Languages* **2019**, *4*, 14

Vygotsky, Lev. 1978. *Mind in Society*. Cambridge: Harvard University Press.

Walther, Joseph B. 1999. Visual cues and computer-mediated communication: Don't look before you leap. Paper presented at the Annual Meeting of the International Communication Association, San Francisco, CA, USA, May 27–31.

© 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*2.9. Differential Expression Analysis*

Results of the statistical analysis, i.e., the list of the differentially expressed genes (DEGs) (*p*-value adjusted (padj) < 0.01) are provided in the DESeq2\_results\_with\_anno.xls file (Table S1). Genes with a fold change >1.5 were taken into account for further bioinformatics analysis. Gene symbol and gene name identification was achieved using Protein ANalysis THrough Evolutionary Relationship (PANTHER) Classification System software (http://www.pantherdb.org/ accessed on 28 July 2021) [34].


*4.1. Human Versus Nonhuman Determination*

Nonhuman remains comprise a significant portion (25–30%) of total cases assessed by forensic anthropologists [1–3] and can represent more than 90% of skeletal cases submitted to medical examiner offices [1]. Although forensic anthropologists mentally assess bone size and shape when determining skeletal species, only one other published study was found that assessed the utility of basic long bone osteometrics in differentiating human from nonhuman remains. Saulsman et al. [14] created discriminate functions from a sample of 50 human and 50 nonhuman specimens from five Australian species. Their study illustrated the potential utility of such quantitative methods, with accuracy rates over 95%, but it was limited by sample sizes and species inclusion.

Our results, where more than 16,000 long bones were assessed quantitatively to develop predictive models, support their findings. From this extensive dataset, we provide discriminant functions and decision trees that can be used to assist or support human versus nonhuman determinations from long bones. Even when all elements are pooled, the DFA and decision trees return over 90% accuracy, with correct classifications of human remains over 95% (99.6% for the decision tree). Thus, high accuracy rates can be achieved even without first distinguishing the specific bony element present. If the bone is first identified and bone-specific methods are applied, accuracy increases further for all models except the tibia-specific and ulna-specific discriminant functions, which were slightly lower. The ulna performed the worst across most analyses, which may partly be due to the lack of distal measurements collected for this element. Generally, the decision tree presented slightly higher overall accuracy rates as compared to the DFAs.

When assessing the human versus nonhuman origin of skeletal remains, we recommend the use of the decision trees presented in this paper and Supplementary Materials compared to the discriminant functions, given (1) their higher accuracy rates, (2) their use of more available data and split-validation, and (3) their lack of statistical assumptions [42]. The better performance of decision trees may also reflect the incorporation of multiple sectioning points into the model (one at each node) as compared to a single sectioning point with discriminant functions. In addition, decision trees provide classification rates

at each of the nodes, providing a more realistic view of accuracy and confidence in the classification for any specific set of measurements. For example, if a bone falls into the node 7 in Figure 1, the results indicate about a 75% probability that the bone is human, despite an overall model accuracy rate of 91%. Decision trees are intuitive, transparent, and easy to apply [40,41,46]. While the concept of decision trees is not new to forensic anthropology [39,40,47–50], the method remains underutilized in practice.

Another advantage to decision tree models is that they allow users to assign higher costs to certain sets of misclassifications [36], in this case to the misclassification of human remains as nonhuman. In forensic anthropology, misclassifying human remains as nonhuman could prevent decedent identification, leaving family members without closure and impeding possible criminal investigations. In contrast, the biggest cost of misclassifying a nonhuman element as human is the unnecessary expenditure of time and resources spent in securing a scene and contacting an expert for final determination. The decision trees presented here assist in reducing the possibility of both of these scenarios. A death investigator called to a scene with a bone could have the decision tree printed on a single sheet of paper (or access it via the OsteoID website on their smartphone) and, using a tape measure, can easily follow the branches of the tree for a preliminary assessment of human versus nonhuman. Because of the integrated misclassification costs, the trees are more likely to incorrectly assign a nonhuman bone as human than vice versa; thus, the result is conservative and anything close to matching human form will be treated as if it is human and of forensic significance until determined otherwise (ideally by a trained forensic anthropologist). At the same time, resources are not wasted on scenes containing remains that are clearly not human. Thus, the models presented here can act as a triaging tool.

While some may argue that all bones discovered should be assessed by a forensic anthropologist, this is not realistic and does not represent current practice. Forensic anthropologists typically receive elements that are believed to possibly be human. Those remains that the finder, law enforcement agent, or those consulted by the law enforcement agent (including physicians and veterinarians) deem as not human are frequently not referred to medicolegal agencies or forensic anthropologists. If referred to medicolegal agencies, their non-anthropological personnel may also determine that the remains received are not human and not worth consulting with a forensic anthropologist. Resources, such as the models and web tool presented here, can assist these individuals who are already undertaking these triaging roles to make more informed decisions. If the decision trees, discriminant functions, visual comparison with the web tool images and/or context of the remains suggest that they may be of human origin, the medicolegal agency and forensic anthropologist should be consulted for final determinations. The forensic anthropologist, in turn, may find these resources useful in supporting their designations or confirming the particular faunal species (discussed below).

Not surprisingly, the most accurate human versus nonhuman functions and decision trees include measurements from multiple regions of the bone, which may not be possible in cases involving fragmented remains. Consequently, the use of only specific bone regions was tested as part of this study for application to larger bone fragments. Univariate analyses were performed on maximum lengths to reflect cases in which erosion to the epiphyses could affect proximal and distal elements. Models were created from only the distal measurements (width and depth) and from only the midshaft measurements (maximum and minimum diameters) for use in cases limited to these fragmented regions. The length and distal epiphyseal region-specific analyses produced higher accuracy rates than the midshaft measurements (except for the ulna). This is expected given that maximum length and distal width were commonly the most important variables in the more inclusive models. For the femoral decision tree, despite inputting all six variables, the tree output only used maximum length and was able to correctly classify over 96% of the total sample and over 99% of the human sample. The region-specific discriminate functions developed per bone (Supplemental Table S12) produced accuracy rates above 85% for all functions except the humeral midshaft (67.1%). These results are slightly higher than the region-

specific DFA results presented by Saulsman et al. [14]. While the results suggest that these models may be useful tools when assessing fragmented remains as human or nonhuman, caution is still warranted given that classification rates are only moderately high, and additional evidence (e.g., presence of morphological features, application of a second method) should be provided to support the conclusion. Saulsman and colleagues [14] also warn against estimating the midshaft location on humeral fragments because deviations 2 cm above or below the actual midshaft location significantly altered their classification rates; results from femoral and tibial deviations were more robust. Application of the models to burned fragments must also consider the possibility of bone shrinkage with the thermal modification [51].

The most conservative approach for assessing the human origin of skeletal remains using osteometrics would be to compare specimen measurements with the minimum, maximum, and 95% confidence intervals for human remains presented in Table 2 and at least preliminarily consider anything that falls within that range, or very close to that range, as potentially human pending further analysis. OsteoID [43] will return images of human bones if the input measurements fall anywhere within the min/max or standard deviation ranges compiled from the sample of >2700 individuals. Practitioners must always consider the small possibility that their unknown specimen can be an outlier, perhaps lying at the extremes of the human distribution which may not have been captured in this study. Pathological conditions that affect body size (e.g., dwarfism, gigantism, etc.), although rare, could also affect results [52,53].

In highly fragmented or taphonomically-modified remains, morphometric and visual assessments may not be applicable. Other evidence, such as cortical bone thickness and trabecular bone density may be factored into the decision [4,54,55], although research by Rerolle et al. [56] suggests that corticomedullary index may not be as distinctive in humans as previously suggested. Several papers state that nutrient foramen location and morphology can assist in human versus nonhuman distinctions [57,58]. Microscopic (histomorphological) or molecular methods can also be utilized [59–63] to determine human origin, but they require greater expertise and specialized equipment, are more time intensive, and are destructive to the specimen [3]. Even histomorphological techniques cannot provide 100% accuracy in distinguishing human from nonhuman species, with certain faunal species (e.g., large mammals) and bone types (e.g., presence of only Haversian bone) shown to be particularly problematic [60]. Publications also differ on opinions of the use of osteon circularity in determining human origin of bone [62,63].

#### *4.2. Species Identification*

The quantitative methods of species identification were less successful than those assessing human origin. While these results are likely impacted by uneven sample sizes across the 28 species, they also reflect morphological and size similarity between some species. For example, brown bear and black bear long bones are morphologically similar [41,64–67], especially as represented by these few basic measurements; thus, small brown bears and large black bears may be misidentified. Sheep and goat long bones are also difficult to differentiate [29,68]. Domestic dogs pose many issues, not just because of their similarity to other canids included in this study (e.g., coyotes and wolves) [69,70] but also because of their high degree of variability in both morphology and size [71,72]. The DFA species classification rates were significantly higher than chance, but the probability of species misidentification remains relatively high. The application of a discriminant function to classify an unknown specimen into one of 28 groups would also be impractical to do by hand, thereby requiring computer usage. Ultimately, practitioners must rely on visual comparisons of more subtle morphological differences in making the final faunal species designations.

In facing these challenges of species identification, the OsteoID website [43] is particularly useful. Users can input basic measurements to narrow down the potential species and are presented with photographic images of the possible identifications.

Thus, the measurements are used as a filtering tool, but the final identification is still based on visual comparison. With the use of visual comparisons, OsteoID can be used for identifying fragmented elements. Supplemental resources provided on the website can also be utilized in skeletal identifications, such as access to the metric database, a link to this publication and associated Supplementary Materials, 3D scans of numerous elements, and lists of other useful texts and websites. Photographs of additional elements (e.g., carpals) not included in the web tool are provided and will be continually updated. The web tool can easily be modified if future minimum/maximum values need revision. There is also the possibility of expanding the database and web tool to include additional species/specimens in the future.

As an online, searchable, comparative osteology collection that includes photographs, data, and 3D scans, OsteoID [43] provides forensic anthropologists with a centralized location for free resources to facilitate skeletal species identification. Practitioners with less zooarchaeological training or lacking access to physical comparative collections will benefit most from these resources when determining faunal species. The web tool and online resources can be accessed from smart phones and other devices while at the scene. With the download of free third-party applications, even the 3D bone models can be viewed on smart phones. The 3D models also can be downloaded and 3D printed to create comparative collections. Beyond forensic anthropologists, forensic pathologists, medical examiners, coroners, crime scene and death investigators, and law enforcement personnel may find OsteoID useful when making preliminary assessments. In situations where scene personnel have reason to believe that remains are nonhuman and typically would have dismissed the remains as not forensically significant, they can use the OsteoID resources to visually confirm that the morphology is not consistent with a human and perhaps find a faunal species match. In cases in which there is any possibility that remains are human, expert opinions should still be obtained. Modified remains or those that are more diagnostically difficult will require a forensic anthropologist's expertise, but OsteoID can reduce time and cost expenditures for diagnostically nonhuman remains. Bioarchaeologists, zooarchaeologists, veterinarians, and biologists may also find the OsteoID web tool and resources useful, and the general public may find interest in learning more about remains encountered. Presently, there are multiple social media groups where individuals post their skeletal finds and group participants provide species identifications. Given that OsteoID is publicly available, it contains multiple disclaimers urging anyone with remains that could potentially be human to leave them in situ and to contact local authorities. Finally, the photographs and 3D scans made available via the website can be used to train students in comparative osteology and the data may be used by researchers in other studies.

## *4.3. Limitations and Future Directions*

Given that all forensic anthropologists rely partly on bone form (i.e., size and general shape) when assessing human origin, using bone metrics to create a quantitative classification method seems simple and logical. However, our study illustrates several challenges to this work. Firstly, it is difficult to find measurements that can be collected consistently across diverse species and bones. Limiting our measurements to maximum lengths, breadths, and depths allowed us to increase the range of animals and skeletal elements in our dataset for pooled analyses, but it excludes aspects of discrete morphological features used in visual assessments of species identification. While the general morphometric variables were able to successfully differentiate human from nonhuman remains (similar to the results of Saulsman et al. [14]), visual assessments that consider specific bone features are necessary for accurate faunal species identification.

Because the methods developed here are dependent on size and epiphyseal breadths, only skeletally mature specimens could be included in quantitative analyses (and resultant functions and models are only applicable to skeletally mature specimens). At least partial fusion of both the proximal and distal epiphyses should be observed prior to utilizing the discriminant functions or decision trees. Skeletally mature specimens

of certain species can be hard to locate, especially domesticated species which may be butchered as juveniles [73]. The species curated at museums vary and again tend not to focus on domesticated species or may not curate full skeletons, especially for larger mammals where space becomes a challenge.

Unequal sample sizes from different species could have biased our classification results, particularly with human versus nonhuman analyses. Although a high degree of faunal variation is captured in the pooled nonhuman sample, there is a smaller representation of some of the largest mammalian species. Given that humans also have relatively large body sizes, this may be driving some of the classification bias, as the models may be more likely to classify all large bones (human or nonhuman) as human given the large human sample sizes. Indeed, larger animals such as moose, brown bear, horse, cow and elk were more commonly misclassified as human, which could explain the relatively higher human and lower nonhuman classification rates in the discriminant functions. Misclassifying some of these species elements as human instead of nonhuman in preliminary forensic contexts is less costly than erroneously classifying human elements as nonhuman; following the preliminary human classification, a forensic anthropologist would then be consulted for a more formal assessment that would identify the error.

The smaller sample sizes in some nonhuman species are also less likely to capture the true population size variation and thus impact DFA species classifications. The human sample size, however, which is of greatest forensic significance, is sufficiently large, and the nonhuman sample sizes exceed those of previous publications [14]. Furthermore, not all measurements were available for all specimens. Data obtained from the literature frequently had some but not all the study measurements, meaning that in the DFAs, many of those cases were excluded.

The species included in the metric database are not exhaustive, and it is unclear how a specimen from an excluded species would classify. This study was limited to species commonly encountered in North America that were accessible at collections but does not include, for example, marine mammals. Further validation of the developed methods is needed, and if more data can be collected from additional species and specimens, revised models may be more appropriate. Future data collection for human versus nonhuman determinations should focus on adding greater samples of larger-bodied mammals. While increased samples of larger-bodied fauna may decrease model accuracy rates, it is possible that the models may still be able to confidently differentiate human from nonhuman specimens given the distinct functional anatomy of humans [3,74,75].

Preliminary analyses using a subsample of the humeral and femoral data suggest that machine learning and random forest models may be able to further increase morphometric classification rates for human versus nonhuman designations and species assignments [76]. Random forest models are a machine learning approach in which numerous decision trees are created from random subsamples, and their predictions are combined through averaging to produce a final classification [46–48]. This machine learning technique increases classification stability and alleviates potential issues of overfitting [58]. The downside of random forest models is their complexity. Because random forest model results are based on the combined results of hundreds or thousands of trees, there is no final model/tree that can be presented or applied to cases [46]. This ensemble approach is considered a "black box" method [41] meaning that it is mathematically complex and difficult to understand and explain in terms of application [77], which can be a disadvantage in court testimony. Furthermore, for broad application, a software program would need to be created to run the random forest models with new unknown specimens.

#### **5. Conclusions**

The tools presented in this study do not diminish the need for forensic anthropologists. Caution must still be used given the high cost of misclassifying a human bone as nonhuman, and forensic anthropologists or other experts should be consulted in situations where there is any possibility that remains may be human. Still, the resources developed and

provided here may be used to preliminarily assess whether remains are potentially human and determine the number of resources to expend on a found bone (e.g., whether or not a scene needs to be preserved, etc.). Forensic anthropologists or other medicolegal personnel can use the resources to support classifications and faunal species identifications. These resources may also be beneficial to other disciplines where skeletal remains are encountered or training in comparative osteology is beneficial, including wildlife forensics, bioarchaeology, zooarchaeology, veterinary medicine, and biology.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/biology11010025/s1. Figure S1: Human versus nonhuman decision tree derived from all available measurements and a pooled-bone sample. Figure S2: Human versus nonhuman decision tree derived from only distal bone measurements and a pooled-bone sample. Figure S3: Human versus nonhuman decision tree derived from only midshaft measurements and a pooled-bone sample. Figure S4: Human versus nonhuman decision tree derived from only maximum length measurements using a pooled-bone sample. Figure S5: Human versus nonhuman decision tree for the humerus, derived from all available measurements. Figure S6: Human versus nonhuman decision tree for the femur, derived from all available measurements. Figure S7: Human versus nonhuman decision tree for the radius, derived from all available measurements. Figure S8: Human versus nonhuman decision tree for the tibia, derived from all available measurements. Figure S9: Human versus nonhuman decision tree for the ulna, derived from all available measurements. Table S1: Descriptive statistics for humeral measurements collected by species. Table S2: Descriptive statistics for femoral measurements collected by species. Table S3: Descriptive statistics for radial measurements collected by species. Table S4: Descriptive statistics for radio-ulnar measurements collected by species. Table S5: Descriptive statistics for ulnar measurements collected by species. Table S6: Descriptive statistics for tibial measurements collected by species. Table S7: Descriptive statistics for fibular measurements collected by species. Table S8: Descriptive statistics for scapular measurements collected by species. Table S9: Descriptive statistics for sacral measurements collected by species. Table S10: Descriptive statistics for pelvic measurements collected by species. Table S11: Descriptive statistics for fused metapodial measurements collected by species. Table S12: Select discriminant functions for human versus nonhuman classification.

**Author Contributions:** Conceptualization, H.M.G., R.D. and S.B.S.; methodology, H.M.G., R.D. and S.B.S.; data collection and processing, H.M.G., R.D., S.B.S., M.S.L., M.M., N.S. and N.K.; formal analysis, H.M.G.; software, H.M.G.; data curation, H.M.G. and R.D.; writing—original draft preparation, H.M.G., R.D., S.B.S.; writing—review and editing, H.M.G., R.D., S.B.S., M.S.L., M.M., N.S. and N.K.; supervision, H.M.G. and S.B.S.; funding acquisition, H.M.G., R.D. and S.B.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Institute of Justice, grant number NIJ 2018-DU-BX-0229. Opinions expressed herein do not necessarily represent the official position or policies of the U.S. Department of Justice or the National Institute of Justice.

**Institutional Review Board Statement:** All data used in this study were collected in compliance with the Declaration of Helsinki and the federal regulations for the protection of human subjects in research at 45 CFR 46 from postmortem skeletal collections curated at established museums or institutions.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are openly available via the OsteoID website (www.boneidentification.com (accessed on 24 December 2021) → Additional Resources), as well as on Dryad (doi:10.5061/dryad.73n5tb2z0).

**Acknowledgments:** We would like to thank all the museum curators that facilitated access to collections. A special thanks to the following individuals who contributed their time or data to the project: Andrea Clendaniel, Elizabeth Dougher, Alexandra Klales, Michael Kenyhercz, Dennis Dirkmaat, Julie Meachen, Chelsea Cataldo-Ramirez, and Christopher Milensky. Finally, thank you to Erin Menardi, the Web Developer who collaborated with the PIs in creating this online tool.

**Conflicts of Interest:** The authors declare no conflict of interest.


3.1.1. Peritectic Reaction

According to Mg-Zr binary phase diagram, Zr and Mg do not form compounds, and there is a peritectic reaction at 653.5 ◦C. The peritectic reaction leads to Mg nucleation at the primary Zr-rich sites, which plays a very important role in the grain refinement process of Mg by Zr [10,13,39]. The peritectic point of Zr composition was previously found to be 0.6% Zr. However, re-assessment shows that it is substantially lower, at about 0.45% [4]. Thus, it is possible to use a smaller amount of Zr, thus reducing the cost of grain refinement [40].

Table 2 presents a comparison between the crystal structures of Mg and Zr, indicating that both α-Zr and α-Mg have HCP crystal structures, and their lattice parameters are quite similar. Thus, Zr has outstanding potency for acting as a nucleation substrate for Mg phase [41]. The perfect nucleation ability of Zr can be further verified by low undercooling (Δ*Tn* ≈ 0.15 ◦C [42]) equal to that for Al grains nucleating on TiB2 substrates (~0.2 ◦C [43]).

**Table 2.** The crystal structure and lattice parameters of Mg and Zr [41].

Figure 3 shows an example demonstrating that 0.5% Zr content can strongly refine the grains of Mg alloy, where the grain size of WE54 Mg alloy (Mg-5Y-4HRE-0.5Zr, HRE = heavy rare earth elements) was refined from 295 μm to 83 μm [44] under the conditions of pouring at about 780 ◦C into a steel mold preheated to 300 ◦C. This content of Zr is basically in accordance with the peritectic point in the Mg-Zr phase diagram. The simulation work performed by Zhao et al. [45] further showed that the growth velocity of dendrite tip of Mg-4Y alloy can be reduced to be one-sixth (1/6) using 0.5Zr, forming a fine grain size. However, this perspective still requires further evidence.

**Figure 3.** An example showing the powerful effect of grain refinement with Zr on Mg alloy. Electron backscattered diffraction technology (EBSD) image of: (**a**) Zr-free WE54 alloy; (**b**) Zr-containing WE54 alloy. Poured at 780 ◦C into a steel mold (300 ◦C). Reprinted with permission from ref. [44]. Copyright 2013 Elsevier.

In early work, it was thought that only the dissolved Zr was effective for grain refinement, because saturated dissolved Zr content meets the standards of a peritectic composition. However, later work verified that Mg grains can nucleate not only onto Zr particles precipitating from the melt during cooling, but also onto undissolved Zr particles through heterogeneous nucleation [4,31,38,46]. The products of peritectic solidification in the microstructure of Zr-containing Mg alloy are Zr-rich halos or cores, present in most Mg grains, as shown in Figure 4 [26,37]. The Zr-rich halo shows either dendritic or nearly spherical growth, depending on the soluble Zr content. A higher level of soluble Zr (close to the solubility) usually leads to spherical halo, while a lower level of soluble Zr leads to a dendritic halo [38].

**Figure 4.** SEM BSE image of Zr-rich cores in: (**a**) Mg-0.56Zr alloy [37]; Reprinted with permission from ref. [37]. Copyright 2002 Elsevier. (**b**) Mg-5Gd-1.5Y-0.55Zr alloy [26]; Reprinted with permission from ref. [44]. Copyright 2013 Wiley.

#### 3.1.2. HRTEM Observations of Zr Nucleus

To facilitate the understanding of the grain refinement mechanism, the orientation relationships (ORs) between Mg grains and Zr nuclei were observed via high-resolution transmission electron microscopy (HRTEM). Table 3 summarizes some of the reported coherent or semi-coherent ORs in grain-refined Mg alloys [23,47–51]. All the ORs have low misfits, which are responsible for the good nucleating potency of Zr. For example, the misfit of OR 1213 *Mg* 1213 *Zr* + 1212 *Mg* ∧ 1◦ 1212 *Zr*, OR 2110 *Mg* 2110 *Zr* + (0001)*Mg* (0001)*Zr* and OR 0111 *Mg* 0111 *Zr* + 1011 *Mg* 1011 *Zr* is only 0.13% [47], 0.9% [49] and 0.41% [50], respectively. Figure 5 shows a typical example of OR 2110 *Mg* 2110 *Zr* + 0111 *Mg* 0111 *Zr* observed in the sand-cast Mg-8Gd-3Y-0.82Zr alloy [23].

1 In Table 3, ∧—tilted directions; —parallel directions; IMS—intensive melt shearing; HPDC—high-pressure die casting.

**Figure 5.** TEM showing OR between Mg matrix and Zr nucleus in Mg-8Gd-3Y-0.82Zr alloy: (**a**) bright-field image; (**b**) dark-field image; (**c**) enlarged view of the selected area in (**a**); (**e**) HRTEM image of the interface between Mg matrix and Zr nucleus (beam 2110 *Zr*); (**d**,**f**) fast Fourier transform (FFT) spectrum of α-Zr and α-Mg, respectively; (**g**) EDS analysis of Zr nucleus. Reprinted with permission from ref. [23]. Copyright 2021 Elsevier.

With the analysis of HRTEM and the selected area diffraction pattern (SADP), Saha concluded that Mg grains only nucleate on the "faceted" crystal planes of Zr particles [47,48], e.g., the basal planes {0001} or the prismatic planes 1010 . However, contradicting Saha, Peng et al. [49] found that the interface between the Zr nucleus and the Mg matrix was "curved" rather than "faceted". The curved interface, in combination with coherent ORs, provides perfect lattice matching along various directions and across various planes, benefitting the nucleation of Mg grains on the Zr particle surface.

Although various ORs have been reported, as shown in Table 3, Yang et al. [51] suggested that very strict ORs are not required for Zr nuclei, because Zr particles may be wetted by Mg melt on all exposed crystal planes. This viewpoint was validated by HRTEM observations in Ref. [49], as shown in Figure 6 [51]. Perfect coherent ORs can be observed, among which even the slightly higher index OR 2423 *Mg* 2423 *Zr* + 1010 *Mg* 1010 *Zr* is present, which is similar to that described in Ref. [27]. Thus, α-Mg grains can grow epitaxially on any suitable planes of Zr nuclei. Recently, in-situ neutron diffraction observations have provided further evidence of the grain refinement mechanism of Zr, showing that with the addition of Zr, all of the diffraction intensities of the 1010 , (0002) and 1011 planes of Mg-5Zn-0.7Zr alloy increase at similar rates during the early stages of solidification, leading to the formation of a uniform grain structure [52].

**Figure 6.** HRTEM images showing: (**a**) faceted interface; (**b**–**d**) curved interfaces between α-Mg grains and Zr nuclei in Mg-0.1Zr alloy along four different low-index zone axes. (**e**) Stereographic projection of *HCP* structure showing the various ORs (**a**–**d**) within a 90◦ range [51]. Reprinted with permission from ref. [51]. Copyright 2015 Elsevier.

## *3.2. Constitutional Supercooling (CS) Effect*

Zr can affect both the nucleation and the growth of the dendritic phase in Mg alloy depending on its status in the melt [53]. When the addition of Zr content is higher, grain refinement can be achieved due to the large number of Zr nucleating particles. When the addition of Zr is low, an obvious grain refinement effect can still be observed. This is mainly due to the strong CS effect of solute [4,10,26,43,54–56], since Zr is also a solute element in the Mg-Zr system. The growth restriction factor (*GRF*, *Q*) is defined as follows to reflect the CS effect [4,43,54]:

$$Q = m(k-1)\tag{2}$$

where *m* is the liquidus gradient and *k* is the partition coefficient. The larger the *Q* value is (unit K or ◦C), the stronger the grain refinement effect will be. On the basis of phase diagram analysis, the *Q* value of Zr was calculated to be 38.29, which is much higher than that of most of the alloying elements, such as Al (4.32), Y (1.70), Zn (5.31), etc. [4,11], indicating that soluble Zr produces a strong grain refinement effect [1,4]. Peng et al. [57] proved that the grain refinement of Mg-9Gd-3Y-0.25Zr alloy (low Zr) was mainly contributed by the *GRF* effect, while the grain refinement of Mg-9Gd-3Y-0.51Zr alloy (high Zr) was mainly contributed by the heterogeneous nucleation of Zr particles. The CS effect of Zr was recently verified by Zhang et al. [57]. As shown in Figure 7, remarkable grain refinement (from 74 μm to 3.5 μm) occurred in the melt pool of the laser-surface-remelted (LSR) Mg-3Nd-1Gd-0.5Zr alloy, which was mostly due to the high CS effect of soluble Zr caused by LSR.

**Figure 7.** SEM BSE images of the laser-remelted Mg-3Nd-1Gd-0.5Zr alloy showing the microstructure of: (**a**) the substrate; (**b**) the melt pool, the cross-section EBSD analysis of which is included as an inset in the upper-right corner [58]. Reprinted with permission from ref. [58]. Copyright 2020 Elsevier.

In summary, the nucleation effect combined with the CS effect leads to a high nucleation rate and low undercooling, resulting in the powerful effect of grain refinement with Zr. Such effects are not only of benefit to achieving equiaxed grains during the casting process, they also facilitate the formation of slurries with fine and spherical primary particles during the semi-solid forming process [59,60].

#### **4. Factors Influencing the Grain Refinement Behaviors of Zr**

This section briefly summarizes the effects that influence the Zr alloying efficiency during the melting process.


**1.1 Classification of dental ceramics**

Classification of ceramics in dentistry is apparently an impossible task due to vast improvements made in the compositions. Nevertheless, the table provided here gives a general idea, say a bird's eye view of ceramics in dentistry.

In cements called glass ionomers, the glass forms the filler and acidic polymers form the matrix. The reaction that proceeds is usually an acid base reaction. Usually they are dispensed as powder containing glass powder, and liquid containing an acid, say, polyacrylic acid. When they are mixed, the acid in liquid etches the glass and reacts with calcium and other ions forming salts and the cement sets to a hard mass. Cements are direct restorative materials – i.e. manipulated and placed onto teeth directly, unlike other restorations that are made outside and fixed to teeth, called indirect restorations. They are highly advantageous as they are quick to set, release fluoride leading to anti-cariogenic

The use of ceramics are encouraged by their biocompatibility, aesthetics, durability and easier customization. The specialty of ceramic teeth is the ability to mimic the natural tooth in colour and translucency along with strength. Ceramics have excellent intraoral stability

Dental ceramics, since introduction have undergone numerous modifications in terms of chemistry. Ceramics have been able to give heed to the ever changing needs in dentistry. To delve deep into the relevance of ceramic in dentistry, one should understand the physics of forces acting in the oral cavity. The masticatory (chewing) force is the strongest force present here. Other minor forces include that of tongue and periodontal ligament, which do

The masticatory force is generated outside oral cavity by basically strong muscles, that move the jaw, open it or close it. Closure of jaw produces two kinds of forces. It is predominantly compressive in nature. Frequently impact kind of force is also experienced. Hence a ceramic has to undergo cycles of these forces indefinitely, without fracture, to result in a successful

In order to have a complete idea of what ceramic means to dentistry, we need to look at the

Classification of ceramics in dentistry is apparently an impossible task due to vast improvements made in the compositions. Nevertheless, the table provided here gives a

Category 1: Glass-based systems (mainly silica)

Category 2: Glass-based systems (mainly silica) with fillers usually crystalline (typically leucite or a different high-fusing

a) Low-to-moderate leucite-containing feldspathic glass b) High-leucite (approx. 50%)-containing glass, glass-ceramics

c) Lithium disilicate glass-ceramics (IPS e.max® pressable and

Category 3: Crystalline-based systems with glass fillers (mainly

Category 4: Polycrystalline solids (alumina and zirconia)

action, esthetic and chemically bond to tooth material.

and wear resistance adding to their durability.

not relate to the use of ceramics in dentistry.

complete range of ceramics used in this discipline.

general idea, say a bird's eye view of ceramics in dentistry.

glass)

alumina)

(Eg: IPS Empress)

machinable ceramics)

restoration of lost teeth structures.

**1.1 Classification of dental ceramics**

**Microstructural Classification**

Table 1. Classification of dental ceramics.

Table 2. Typical oxide composition of dental porcelain.

Ceramics in Dentistry 207

Similarly, ceramic teeth are manufactured in various shades, shapes and sizes to be used in complete dentures. Also, in case of gum recession, in fixed prosthesis, pink coloured

Chemical composition of these ceramics is based on silica network and potash feldspar (K2O.Al2O3.6SiO2) or soda feldspar (Na2O.Al2O3.6SiO2) or both. Potassium and sodium feldspar are naturally occurring minerals composed primarily of Potash and soda. The most important property of feldspar is its tendency to form crystalline mineral leucite when

Fig. 4. Scanning electron micrograph of amorphous glass (Russel Giordano et al, 2010).

ceramics are placed in the lost gum region to make it look natural.

**1.2.1 Category 1: Glass-based systems (mainly silica)**

Fig. 3. All Ceramic Crown.

melted.

The classification based on microstructure will be dealt with in detail and an idea of classification by processing technique is added in this review. Other classifications are of academic interest.

Basically ceramics are used as indirect restorative materials such as crowns and bridges, Inlays/Onlays and dental implants. Recently ceramic braces are used in orthodontics.