An investigation of the three tone system in Tsuut’ina (Dene)
Bibliographic record
Abstract
This study is part of the documentation and conservation of Tsuut’ina (formerly Sarcee, Sarsi; ISO 639-3: srs), a northern Dene (Athabascan) language by a collaboration of academic and community members. Tsuut’ina is a tone language. Contrary to Dene tonogenesis theory and unlike reports on all other Dene tone languages, Tsuut’ina is reported to have three tones, H, L, M. The tonal system in Dene family has been argued to arise from the loss of laryngealized sonorants in monosyllabic stem codas and incorporation of laryngealization into the nucleus of the stem, resulting in H and L tonal contrasts. The Dene languages additionally exhibit “tonal reversal”, a tendency for the Dene tone languages to show “reversed” tonal patterns that postdate the original tonogenesis. In this study we investigate the tonal distribution, realization patterns and tonal alignment in data collected from two fluent speakers reciting prepared wordlists and short discourses. Preliminary investigation indicates that, as reported, three tonal patterns emerge, M tone associated most often with a falling tone, with distinct distribution patterns arguably related to morphological factors. Furthermore M tone is more highly variable. We lay out distribution patterns and interactions with morphology and statistical analyses associated with the data.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".