The Analysis of Coda Clusters in Jizani Arabic: An OT Perspective
Bibliographic record
Abstract
This paper explains how native speakers of Jizani Arabic (henceforth, JA) treat final consonant clusters in superheavy syllables (CVCC) using a parallel Optimality Theory (Prince & Smolensky, 1993, 2004) to show how the theory can account for the cross-linguistic variations of coda clusters through the ranking of different constraints. JA is a Saudi dialect spoken in the southwestern part of Saudi Arabia in Jizan city. It is common among many Saudi Arabic dialects like Najdi, Hijazi, Taifi and Qassimi that rising sonority in coda clusters is avoided by using vowel epenthesis to comply with the Sonority Sequencing Principle (henceforth, SSP), where there is no difference between nasals and liquids. However, in JA, we observe that vowel epenthesis occurs only if the last segment in CVCC is a liquid (/l/ or /ɾ/); for instance, /tʕifl/à [tʕifil] ‘child’ and /ħibɾ/à [ħibiɾ] ‘ink’. The vowel has been epenthesized because the last consonant in both examples is more sonorous than the preceding obstruents. However, the vowel will not be inserted if the final consonant is a nasal preceded by an obstruent; for instance, /laħm/à[laħm] ‘meat’ and /ɡutʕn/à[ɡutʕn] ‘cotton’. Although the universal sonority scale ranks nasals as more sonorous than obstruents, nasals in JA behave as they are equally sonorous as obstruents. In other words, nasals in this dialect group with stops and fricatives in the sonority scale.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".