Bibliographic record
Abstract
Abstract This chapter presents a survey of vowel harmony (VH) in languages of India, focusing on examples from the Indo-Aryan, Tibeto-Burman, and Dravidian families, along with the isolate Burushaski. It sketches the typological properties of harmony, while also drawing generalizations about language families. The survey covers well-known case studies like Assamese, Bengali, and Telugu, but also lesser-known examples. It reveals that VH systems are primarily regressive in India. Common types of VH in the region include height, tongue-root, laxing, and palatal harmony. In tongue-root harmony systems, [+ATR] vowels are dominant, [−ATR] vowels are recessive, and [ɑ] is an opaque blocker. Other blocking effects are attributed to intervening coda consonants, nasals, and stress. The domain of harmony is most often the inflected word. In sum, the survey presents an overview of VH in India based on research to date while also highlighting outstanding issues that might guide future investigations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".