(13-08-2024, 12:17 PM)obelus wrote: You are not allowed to view links. Register or Login to view.@Torsten:
My apologies for attributing to you a prediction that you did not make; and my thanks for planting a different idea. Would not self citation necessarily obey one-way causation of textual novelty—as you describe—but on other scales? On individual pages we have some confidence about the order of composition, and a scribe could not have cited words that they had not yet created. The MMED plot for your simulated text clearly shows this causal horizon progressing down the pseudo-pages. The trend would be baffling if observed in a conventional "linguistic" text.
Of course texts generated by human computers will contain various confounds, but their past will not have been affected by their future, so we potentially could find telltale indicators of citation-with-variation in their time-ordered output. My first-attempt plot for Takahashi pages is not definitive... but if a refined treatment (with a better autocorrelation function, more appropriate transcription, whatever) were to find a statistically significant slope, then I would be statistically significantly persuaded that you are on the right track.
Indeed, in a text generated by self-citation it must be possible to observe a gradual evolution over time.
"It is possible to distinguish Currier A and B based on frequency counts of tokens containing the sequence <ed>. The summary in Table 2 shows, e.g., that if <chedy> is used more frequently, this also increases the frequency of similar words, like <shedy> or <qokeedy> .... At the same time, also words using the prefix <qok-> are becoming more and more frequent, whereas words typical for Currier A like <chol> and <chor> vanish gradually. Now, reordering the sections with respect to the frequency of token <chedy> replaces the seemingly irregular mixture of two separate languages by the gradual evolution of a single system from 'state A' to 'state B'. Since words typical for Currier A also exist in Currier B, but not the other way round, it is reasonable to assume that the order shown in Table 2 indeed represents the original sequence in which the sections of the VMS had been created" [Timm & Schinner 2020, p. 6].
Another observation is that a word type tends to be frequently used only in conjunction with similar words. This occurs because a more frequently used word token is more often selected as a source word, leading to the generation of additional similar word tokens. As these similar tokens accumulate, the likelihood of the word being generated again increases, reinforcing the pattern. This is also observable for the Voynich text since "high-frequency tokens also tend to have high numbers of similar words. This is illustrated in greater detail in Figure 3: 'isolated' words usually appear just once in the entire VMS while the most frequent token [daiin] (836 occurrences) has 36 counterparts with edit distance 1" [Timm & Schinner 2020, p. 6].