Vowel Harmony In Dagbani Dialects
Bibliographic record
Abstract
Harmony system requires that two or more not-necessarily-adjacent segments must be similar or must resemble each other with respect to some feature(s).The paper explores Dagbanli harmony system by describing how Autosegmental Representations account for harmony between a stem and a nominal suffix in a derivational process in which the domain-final /o/ is caused to change underlying root vowel /i/ from [-round] to [+round] to harmonize with the stem vowel.Further representations give accounts of the primary features which reveal that Dagbanli has bi-directional harmonic spread.The focus here is on harmonic features such as round, back as well as vowel copy.Based on the data, the paper attest that Dagbanli exhibits the canonical harmony pattern of [VF ...VF ...VF...VF] and suggests that two of the dialects deviate from this pattern.The paper concludes that while Tomosili dialect permits harmonic canonical pattern, Nayahili and Nanunli deviate by presenting polarity.
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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".