16-03-2026, 06:12 PM
Thank you for the detailed analysis.
A few comments:
Your finding 2 — that similar words cluster in the same vertical area but not in exact columns — is consistent with an observation from Timm (2014). My results point in the same direction as your results: the scribe's eye tends to scan upward rather than strictly horizontally, producing local vertical clustering without exact column alignment.
Your finding 4 is interesting: the first word in a burst tends to be rare (~6) while the variants are frequent (~70). This could be an artifact of the definition of "core" as the first word in the sequence. If the scribe generates text by modifying words already visible on the page, the common words are the established source vocabulary — they are frequent precisely because they have been copied and modified many times. A rare word appearing near them is more likely to be a new modification the scribe just produced from those common forms. In other words, the frequent words may be the actual sources and the rare first word the output, which would invert the direction of generation relative to your burst model.
Your overall conclusion — "some kind of local variant generation mechanism" with "short textual memory" — is consistent with the self-citation model described in Timm (2014) and Timm & Schinner (2020).
A few comments:
Your finding 2 — that similar words cluster in the same vertical area but not in exact columns — is consistent with an observation from Timm (2014). My results point in the same direction as your results: the scribe's eye tends to scan upward rather than strictly horizontally, producing local vertical clustering without exact column alignment.
Your finding 4 is interesting: the first word in a burst tends to be rare (~6) while the variants are frequent (~70). This could be an artifact of the definition of "core" as the first word in the sequence. If the scribe generates text by modifying words already visible on the page, the common words are the established source vocabulary — they are frequent precisely because they have been copied and modified many times. A rare word appearing near them is more likely to be a new modification the scribe just produced from those common forms. In other words, the frequent words may be the actual sources and the rare first word the output, which would invert the direction of generation relative to your burst model.
Your overall conclusion — "some kind of local variant generation mechanism" with "short textual memory" — is consistent with the self-citation model described in Timm (2014) and Timm & Schinner (2020).
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