Phonotactic constraints and learnability: analyzing Dagaare vowel harmony with tier-based strictly local (TSL) grammar
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it