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’.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".