22-11-2019, 11:35 AM
It is known that Eva-f and Eva-p appear most commonly on top lines of paragraphs, but how many exceptions are there?
Using the capabilities mentioned in You are not allowed to view links. Register or Login to view. , it was easy to check.
I used my own transcription (version 1c), and removed uncertain spaces. For alternative readings I selected the first.
Any rare characters that look (a bit) like f and p were ignored, but cPh and cFh were included.
Beside normal paragraph text, these characters also appear in labels, and to some extent in text in circles. The whole text was therefore grouped into three categories:
- First lines of paragraphs
- All other lines of paragraphs
- All other text
The entire text has been represented into 5389 text items or 'loci'. Of these, there are:
723 first lines of paragraphs
3407 other paragraph lines
1259 other types
These loci have different lengths, so the statistics are based on word tokens. I add a screen shot of an Excel file:
[attachment=3725]
The column 'all' gives the count of all word tokens, and the next two the count of tokens including at least one f or one p.
The row 'P-1' gives the first lines of paragraphs and 'P-n' all other lines. The line 'Other' gives all other loci.
Overall, there are about 3 times as many p-words as f-words. Their distribution in the non-paragraph text is rather similar to the overall text of the MS, but for the paragraph text the know behaviour is quite pronounced. The percentage is more than a factor 10 higher.
However, the exceptions are not rare. There are almost 400 occurrences on later lines in paragraphs, which is again something that requires an explanation. These are almost certainly not just mistakes, as if their appearance there would be 'forbidden'.
How this compares between A and B languages is a next step.
Using the capabilities mentioned in You are not allowed to view links. Register or Login to view. , it was easy to check.
I used my own transcription (version 1c), and removed uncertain spaces. For alternative readings I selected the first.
Any rare characters that look (a bit) like f and p were ignored, but cPh and cFh were included.
Beside normal paragraph text, these characters also appear in labels, and to some extent in text in circles. The whole text was therefore grouped into three categories:
- First lines of paragraphs
- All other lines of paragraphs
- All other text
The entire text has been represented into 5389 text items or 'loci'. Of these, there are:
723 first lines of paragraphs
3407 other paragraph lines
1259 other types
These loci have different lengths, so the statistics are based on word tokens. I add a screen shot of an Excel file:
[attachment=3725]
The column 'all' gives the count of all word tokens, and the next two the count of tokens including at least one f or one p.
The row 'P-1' gives the first lines of paragraphs and 'P-n' all other lines. The line 'Other' gives all other loci.
Overall, there are about 3 times as many p-words as f-words. Their distribution in the non-paragraph text is rather similar to the overall text of the MS, but for the paragraph text the know behaviour is quite pronounced. The percentage is more than a factor 10 higher.
However, the exceptions are not rare. There are almost 400 occurrences on later lines in paragraphs, which is again something that requires an explanation. These are almost certainly not just mistakes, as if their appearance there would be 'forbidden'.
How this compares between A and B languages is a next step.