Bibliographic record
Abstract
Despite substantial phonological research into segmental co-occurrence patterns, there is currently no systematic way of calculating the gradient degree to which a segment participates in a harmony system, across its co-occurrences with all other segments. In this paper, I adopt the statistical concept of relative risk as a measure of participation in harmony. I compute both O/E values and the relative risk measure for vowels in corpora of three languages with front/back harmony: Chuvash, Tatar, and Mari. I show that relative risk corresponds to the intuitive notion of how much a vowel participates in harmony, viewed based on how regularly it occurs in disharmonic contexts. I then consider the implications of the results, given what is known about categorical trends of participation in front/back harmony systems in other languages. For example, the relative risk values show that [i] generally participates less in the harmony system than most other vowels in all of these languages, and that marked vowels are typically highly harmonic. As such, this measure can illuminate gradient language-internal and cross-linguistic patterns in harmony participation that are not apparent from more categorical descriptions or entirely clear from O/E values.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".