Phonotactic constraints and learnability: analyzing Dagaare vowel harmony with tier-based strictly local (TSL) grammar
Bibliographic record
Abstract
Abstract This paper examines vowel harmony in Dagaare using the Tier-Based Strictly Local (TSL) framework, focusing on tongue root, rounding, backness, and height harmonies. While vowel harmony in Niger-Congo languages, particularly Dagaare, has been explored from phonetic, phonological and typological perspectives, computational insights remain limited. The study applies the TSL framework to model the phonotactic constraints governing harmonic patterns, projecting only harmony-relevant features onto a tier to capture non-local dependencies while ignoring irrelevant segments. This approach allows for precise modeling of agreement among non-adjacent vowels and demonstrates that all harmony types in Dagaare can be represented within a single TSL grammar, avoiding the need for separate tiers for each feature. The findings indicate that the Dagaare system is robustly learnable from surface data under TSL constraints, offering a computationally tractable path for both human and machine learners. A key limitation is also identified: TSL fails to account for harmony exceptions in morphologically complex words, such as compounds, due to its lack of morphological domain sensitivity. The study contributes to the typological understanding of Dagaare, illustrates the utility of TSL for modeling complex harmony systems, and recommends extensions such as domain-sensitive tier projection to better handle morphologically complex contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".