[split] Word length autocorrelation

[quimqu]

I tried comparing different ways of computing the lag-1 autocorrelation of word lengths in the Voynich:

• Global sequence (all words concatenated): +0.16
  → same result as Gaskell & Bowern, since long lines dominate and cross-line pairs are included.
  
• Line-by-line average (each line treated independently, then averaged equally): –0.07
  → negative, like what is usually seen in natural languages.
  
• Line-by-line weighted average (weighted by the number of word pairs per line): –0.03
  → almost neutral, but still slightly negative.
  

So the discrepancy comes from how the calculation is done:

• Looking at the whole corpus, the Voynich shows positive autocorrelation.
  
• Looking line-by-line, most sections, Currier hands, and scribal hands show negative autocorrelation.
  
• Weighting by line length pushes it closer to zero.
  

This suggests that cross-line word pairs and the dominance of longer lines are what make the global measure flip to positive, while within lines the tendency is closer to the short-term negative correlation typical of natural languages.


I have done the same calculations for different languages and a Torsten Timm generated text:

Text	Global (within+cross)	Within-line	Cross-line	Weighted mean (per line)
Voynich (EVA)	+0.16	–0.07	+0.10	–0.03
Timm (generated)	+0.02	+0.03	+0.02	–0.07
Platonis Apologia (Latin)	–0.09	–0.09	–0.08	–0.17
Unfortunate Traveller (English)	–0.11	–0.11	–0.12	–0.21
Lazarillo de Tormes (Spanish)	–0.19	–0.19	–0.19	–0.27

All three natural language texts (Latin, English, Spanish) show negative autocorrelation across the board, both globally and line by line. 
Timm’s generated text shows near-zero / slightly positive global autocorrelation, but negative when measured per line.
The Voynich manuscript is the outlier: positive globally (+0.16), but negative line by line (–0.07 or –0.03 weighted).

The Voynich combines two opposing tendencies:
- At the global corpus level, it looks like Timm’s generated text (positive autocorrelation, even more unlikely than natural languages).
- At the line level, it behaves more like natural languages (negative autocorrelation).
- The difference between within-line and cross-line autocorrelation in the Voynich seems key. In natural languages, line breaks do not affect the measure, but in the Voynich they do: within-line is –0.07 while cross-line is +0.10, a gap of 0.17. This difference is unlikely to be accidental and suggests that line breaks play a structural role in how the text was generated.

This split behavior could be a clue that the line structure plays a central role in how the text was generated.