Bibliographic record
Abstract
Abstract This chapter explores fundamental ideas and challenges in using statistical and computational methods for the analysis of vowel harmony from text corpora. These methods are centered on the quantification and visualization of the degree and type of harmony in a language, which can be used for cross-linguistic comparison, measuring historical change in harmony, and unsupervised identification of previously unknown harmony patterns. Models using these methods can differ greatly in robustness, interpretability, and generality. A successful and appropriate model requires consideration of many factors, including the types of corpora to be analyzed, the effect of morphological structure and phonological alternations on harmony, and the potential for anomalous patterns in proper names, loanwords, onomatopoeia, and similar types of words. The chapter includes a general overview of data-driven approaches, important considerations in selecting and using data sources, linguistic considerations and how different models handle them, and work on data-driven visualizations of harmony.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".