Bibliographic record
Abstract
This article compares three hiatus resolution strategies, viz., glide formation, secondary articulation and vowel elision in Karanga and Nambya, two southern Bantu languages spoken in Zimbabwe. The overall analysis is couched in Optimality Theory (hereafter OT). The strategies operate across a prefix and a stem as well as across a nominal stem and a diminutive suffix. In both languages, glide formation is the default strategy and when blocked by phonotactic constraints, secondary articulation kicks in. In turn, when secondary articulation is blocked by OCP-driven constraints, V1 elision occurs. The main inter-language difference occurs when V1 is a coronal vowel and is preceded by a consonant; Karanga deletes V1 regardless of the quality of the preceding consonant because it does not allow palatalized consonants. In contrast, Nambya which allows some palatalized consonants employs secondary articulation with all other consonants except when the preceding consonant is palatal–where V1 is elided. In sum, in Karanga and Nambya, the quality of V1 and whether it is preceded by a consonant or not as well as the type of consonant preceding it determine which strategy between glide formation, secondary articulation and elision repairs the dispreferred configuration-hiatus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".