Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an extension of Baxter & Manaster-Ramer’s (2000) approach to the problem of false cognates in the determination of relationships between languages. Their approach uses a Monte Carlo simulation to estimate how many lexical similarities we can expect to be due to chance between two lexical lists from different languages, and consequently how many are too many to be all false cognates. Although very efficient, their model has the shortcoming of being applicable only to simple lexical lists such as the Swadesh list, with one-to-one semantic correspondences between the individual terms. Here I present a new model that can be applied to any kind of word list, and can include comparisons between multiple terms sharing the same semantic field. After a theoretical description, a controlled test and a contra-test, I finally apply the method to a real test case, investigating the probability of relation between Pre-Greek, the nonIndo-European substrate of classical Greek, and Proto-Basque, Proto-Uralic and ‘Proto-Altaic’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it