Bibliographic record
Abstract
The phonology of several Kuki-Chin (South Central Trans-Himalayan) languages have been described well, and there are fragmentary sketches of numerous others. Extensive diachronic work has also been done for the languages of this group. However, there is no comprehensive survey of the synchronic phonologies of Kuki-Chin languages. This chapter attempts to fill that gap so that researchers working on one of these languages, or doing broader typological surveys, can easily grasp the broad sound patterns in, and phonological questions raised by, Kuki-Chin. The chapter covers syllable structure, onsets, rhymes, and morphophonology. Onsets and rhymes are illustrated with complete inventories for Proto-Kuki-Chin and six attested Kuki-Chin languages from various subgroups (Falam, Mara, Thado, Daai, Lemi, Sorbung, and Monsang) and a comparative perspective on each of these inventories. This is followed by a discussion of the broader issues in Kuki-Chin sound inventories and phonotactics. These issues include laryngeal contrasts in obstruents and sonorants, the special status of glottal stop, and vowel length distinctions. A range of morphophonological alternations are then addressed, including the widespread phenomenon of non-final shortening (illustrated with observations from Thado, Daai, Sorbung, Falam, and Zophei) and vowel harmony (attested in at least Lamkang and Hyow). Apophony in stem form alterations and transitivity alternations is also discussed, drawing largely on data from Hakha Lai.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".