Chapter 9. Be careful what you throw out: Gemination and tonal feet in Weledeh Dogrib
Bibliographic record
Abstract
The Weledeh dialect of Dogrib (Tłįchǫ Yatiì) is spoken by people of the Yellowknives Dene First Nation, in and around Yellowknife, Northwest Territories. Within the formal framework of Lexical Phonology (Kiparsky 1982), this paper argues for an over-arching generalization in the phonology of Weledeh Dogrib: the constraint NoContour-Ft, which prefers (High-High) and (Low-Low) feet, but militates against (High-Low) and (Low-High) feet. NoContour-Ft is satisfied differently in different morphophonological domains: vowel deletion at the Stem Level, gemination at the Word Level, and High to Mid tone lowering at the Postlexical Level. This analysis requires that consonant length be treated as phonological in Dogrib—that is, consonant length contributes to syllable weight and mora count—even though there are no minimal pairs based on consonant length. Similarly, the distinction between High and Middle tone does not distinguish any lexical items, but is nevertheless important for the prosody of the language. Thus the paper makes a methodological point about the importance of allophonic alternations for phonological theory. Our view of what counts as contrastive or allophonic, however, is to a large extent theory-dependent; therefore, the paper also emphasizes the importance of phonetic measurements when doing fieldwork.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".