Phylogenetic Methods in Historical Linguistics: Greek as a Case Study
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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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".