voynichrose > 13-03-2025, 01:29 AM
voynichrose > 13-03-2025, 02:51 AM
Quote:ABSTRACT:
The cultural significance of researching the House of Lancaster’s hidden military codes redefines how a small little country in the North could become a world Empire. In analyzing Yale MS 408 and understanding the cypher and who created it, it uncovers a lot of political dynamics within Europe during the Middle Ages. This study used cryptography of perceptual dialectology through etymology by analyzing the entire European Royal Families Blood line. The result was an astonishing uncrackable code still relevant to today’s standards used to hide Royal, Catholic and Templar Secrets. Yale MS 408 is the calling card of House Lancaster because of evidence found within the royal families signatures that are the only known symbols to match 100% in the manuscript.
voynichrose > 13-03-2025, 03:09 AM
voynichrose > 13-03-2025, 06:15 AM
Quote:Abstract The text of the Voynich Manuscript (VMS) has often been regarded as too non-random to be meaningless. However, if the VMS is indeed a hoax, it was probably not produced by a purely random process but rather by some form of automatic writing or glyptolalia in which the scribe(s) simply invented meaningless text as they went based on an intuitive impression of what written language ought to look like. Here, we show that such intuitive “gibberish” is significantly non-random and in fact exhibits many of the same statistical peculiarities as Voynichese. We recruited 42 volunteers to write short “gibberish” documents and statistically compared them to several transcriptions of the VMS and a large corpus of linguistically meaningful texts. We find that “gibberish” writing varies widely in its statistical properties and, depending on the sample, is able to replicate either natural language or Voynichese across nearly all of the metrics which we tested, including traditional criteria for identifying natural language such as Zipf’s law. However, gibberish tends to exhibit lower total information content than meaningful text; higher repetition of words and characters, including triple repeats; greater biases in character placement within lines and word placement within sections; positive autocorrelation of word lengths (i.e., a tendency for words to cluster short-short-longlong rather than short-long-short-long); and a weaker average fit to Zipf’s law. The majority of these properties are also observed in Voynichese. A machine-learning model trained to distinguish meaningful text from gibberish in our dataset identified most VMS transcriptions as more closely resembling gibberish than meaningful text. We argue that these results refute the idea that the low-level linguistic structure of the VMS text is too non-random to be meaningless. However, our writing samples are too short to test whether the higher-level structure of VMS pages and quires could also be produced by gibberish.
Dana Scott > 13-03-2025, 07:43 AM
(13-03-2025, 06:15 AM)voynichrose Wrote: You are not allowed to view links. Register or Login to view.This just came out 2 years ago and was a study done on the voynich if it were gibberish.
Gibberish after all? Voynichese is statistically similar to human produced samples of meaningless text Daniel E. Gaskell 1 and Claire L. Bowern 1 1 Yale University, PO Box 208236, New Haven, CT 06520, USA
Quote:Abstract The text of the Voynich Manuscript (VMS) has often been regarded as too non-random to be meaningless. However, if the VMS is indeed a hoax, it was probably not produced by a purely random process but rather by some form of automatic writing or glyptolalia in which the scribe(s) simply invented meaningless text as they went based on an intuitive impression of what written language ought to look like. Here, we show that such intuitive “gibberish” is significantly non-random and in fact exhibits many of the same statistical peculiarities as Voynichese. We recruited 42 volunteers to write short “gibberish” documents and statistically compared them to several transcriptions of the VMS and a large corpus of linguistically meaningful texts. We find that “gibberish” writing varies widely in its statistical properties and, depending on the sample, is able to replicate either natural language or Voynichese across nearly all of the metrics which we tested, including traditional criteria for identifying natural language such as Zipf’s law. However, gibberish tends to exhibit lower total information content than meaningful text; higher repetition of words and characters, including triple repeats; greater biases in character placement within lines and word placement within sections; positive autocorrelation of word lengths (i.e., a tendency for words to cluster short-short-longlong rather than short-long-short-long); and a weaker average fit to Zipf’s law. The majority of these properties are also observed in Voynichese. A machine-learning model trained to distinguish meaningful text from gibberish in our dataset identified most VMS transcriptions as more closely resembling gibberish than meaningful text. We argue that these results refute the idea that the low-level linguistic structure of the VMS text is too non-random to be meaningless. However, our writing samples are too short to test whether the higher-level structure of VMS pages and quires could also be produced by gibberish.
Koen G > 13-03-2025, 09:14 AM
voynichrose > 13-03-2025, 09:46 AM
voynichrose > 01-04-2025, 09:18 AM
voynichrose > 01-04-2025, 04:38 PM
nablator > 01-04-2025, 07:23 PM