Phylogenetic Methods in Historical Linguistics: Greek as a Case Study
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We review and assess the different ways in which research in evolutionary-theory-inspired biology has influenced research in historical linguistics, and then focus on an evolutionary-theory inspired claim for language change made by Pagel et al. (2007). They report that the more Swadesh-list lexemes are used, the less likely they are to change across 87 Indo-European languages, and posit that frequency-of-use of a lexical item is a separate and general mechanism of language change. We test a corollary of this conclusion, namely that current frequency-of-use should predict the amount of change within individual languages through time. We devise a scale of lexical change that recognizes sound change, analogical change and lexical replacement and apply it to cognate pairs on the Swadesh list between Homeric and Modern Greek. Current frequency-of-use only weakly predicts the amount of change within the history of Greek, but amount of change does predict the number of forms across Indo-European. Given that current frequency-of-use and past frequency-of-use may be only weakly correlated for many Swadesh-list lexemes, and given previous research that shows that frequency-of-use can both hinder and facilitate lexical change, we conclude that it is premature to claim that a new mechanism of language change has been discovered. However, we call for more in-depth comparative study of general mechanisms of language change, including further tests of the frequency-of-use hypothesis.
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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 | 0.003 | 0.034 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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