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Record W4224305895 · doi:10.5539/ijel.v12n3p89

The Analysis of Coda Clusters in Jizani Arabic: An OT Perspective

2022· article· en· W4224305895 on OpenAlexvenueno aff
Raneem Bosli, Lynne Cahill

Bibliographic record

VenueInternational Journal of English Linguistics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsSonority hierarchyObstruentCodaConsonantLinguisticsVowelConsonant clusterArabicMathematicsHistoryPhilosophyPhysicsAcoustics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.101
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.101
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.349
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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