Revisiting the morphophonology of Thangmi: a Tibeto-Burman language of Nepal
Bibliographic record
Abstract
This article revisits the morphophonology of Thangmi, a Tibeto-Burman language spoken in Nepal by a community group of the same name whose grammar and lexicon I was involved in documenting from 1996 onwards. The Thangmi (Nepali Thāmī) are an ethnic group who number around 30,000 and inhabit the central eastern hills of Nepal. The Thangmi are autochthonous to the upper reaches of Dolakhā district as well as to the eastern valleys of Sindhupālcok district, and their hitherto undocumented Tibeto-Burman language has two distinctly recognisable and mutually unintelligible dialects. Morphophonology (also known as morphophonemics) explores the interaction between morphology and phonology, and is predicated on a rigorous investigation of the phonological variations within morphemes that oftentimes mark different grammatical functions. While complex, Thangmi morphophonology lends itself to transparent interpretation, and this paper offers a modified analysis that builds on and develops from my earlier work (Turin 2012, 2005). Following a brief introduction to Thangmi segmental phonology, this article covers four aspects central to Thangmi morphophonology: the remnants of what may be a defunct liquid-nasal alternation, a brief overview of assimilation, a robust review of intervocalic approximants and finally a brief note on syncope.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".