occurs in the largest fraction of words, and so for any comparable shift in rank or frequency, the measures undergo a lesser rescaling due to the normalization process, since $\SySsub$ is smaller earlier in the discovery process.

Perhaps most interestingly, we see that the change count index $\delta_r$ reports higher innovation for larger symbol lists, in a way approximately with its definition, but this behaviour is suppressed for $\delta_\omega$, and even reversed for the nonlinear measure $\delta_\chi$. This implies that the discovery process is more incremental for larger $\SySize$, involving relatively few(er) discoveries that substantially revolutionize the size and structure of the currently known sub-dictionary. This is not unexpected, since in F-dictionaries with larger word counts, the whole set of symbols will be better and more evenly sampled.

Since the above results were calculate for a fixed word length, in fig. \[fig-fixed-parscaling\] I instead hold the dictionary size $\DcSize$ fixed at $1024$, and vary word length $\pWordLen$. There is a clear trend for higher innovation for shorter words, and the trend for suppressed innovations $\delta_\omega$ and $\delta_\chi$ at larger $\SySize$ systematically fades away for short words, and even reverses for the shortest word lengths. This is most likely caused by 