Moro voicelessness dissimilation and binary<i>[</i>voice<i>]</i>
Bibliographic record
Abstract
This paper reports on a pattern of voicelessness dissimilation in the Kordofanian language Moro. Voiceless stops and affricates become voiced before a voiceless obstruent in a transvocalic configuration. The dissimilation is robust, and productive across morphological contexts. Phonetically, voicing in Moro is realised as a difference between prevoicing and short-lag voice onset time. This makes [voice] the most realistic featural characterisation; using another feature like [spread glottis] in lieu of [–voice] doesn't explain the contrast. Consequently, dissimilation of voicelessness in Moro is strong evidence that [voice] is a binary feature, and that […voice] may be phonologically active despite being ‘unmarked’. We show that when [–voice] is admitted, the Moro pattern is straightforwardly analysed on a par with other cases of dissimilation. Our analysis uses the theory of surface correspondence, which carries no crucial assumptions about markedness; other theories of dissimilation are considered in an online supplement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".