Perception of ATR in Dagaare [daɡaarɪ]
Bibliographic record
Abstract
This paper reports on two related perception studies about the property Advanced Tongue Root (ATR) in Dàgáárè (Mabia; Ghana). We examine how well native speakers are able to distinguish ATR contrasts as well as the effects of harmony and disharmony on perception, thereby testing hypotheses that have been made in the literature about the perceptual motivations of harmony systems. We find that, as expected, ATR mid vowels and Retracted Tongue Root (RTR) high vowels are the hardest to distinguish in Dàgáárè, but contrary to expectations, harmony does not improve accuracy in discriminating ATR contrasts. Nonetheless, we find the accuracy on disharmonic disyllabic forms is significantly worse than the accuracy in monosyllabic forms, which may indicate that disharmony hurts perception. We examine the implications for our understanding of the motivations of harmony systems and discuss how this paper contributes to the very minimal existing literature on perception in African languages.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".