Investigating the universality of consonant and vowel co-occurrence restrictions
Bibliographic record
Abstract
Certain phonotactic constraints on the co-occurrence of segments appear to be much more common across the world’s languages than others. In many languages, similar consonant co-occurrence is restricted through Obligatory Contour Principle (OCP) effects, while there are some exceptions for identical consonants. In vowels, the opposite pattern appears to hold: many languages have vowel harmony processes, where vowels within a domain are required to share some feature. Languages that encourage similar consonant co-occurrence or restrict similar vowel co-occurrence appear to be exceedingly uncommon. However, evidence of this pattern so far only comes from studies of individual languages or families, or of only consonants or vowels. We investigate patterns of co-occurrence in vowels and consonants in 107 Northern Eurasian languages across 21 families using Bayesian negative binomial regression to explicitly model the effects of aggregate similarity and segment identity on co-occurrence counts (the results of which can be interpreted similarly to observed/expected ratios). We find that the effect of similarity is remarkably consistent across languages: Similar consonant co-occurrence is disfavored, while aggregate similarity has no effect on vowel co-occurrence. Identical segment co-occurrence effects are much more variable across languages, with a tendency towards disfavoring identical consonants, and favoring identical vowels. We also find larger effects in consonants than in vowels, suggesting that consonant co-occurrence is more strongly constrained than vowel co-occurrence. We also find that there is no evidence for or against any correlations between vowel and consonant co-occurrence, suggesting that more data is needed to evaluate this possibility.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".