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Record W3118459711 · doi:10.33806/ijaes2000.20.2.5

Quantifying Nasality in Arabic Speakers: Preliminary Data

2020· article· en· W3118459711 on OpenAlexaboutno aff
Yaser S. Natour

Bibliographic record

VenueInternational Journal of Arabic-English Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsNasalityVowelPsychologyArabicNormativeAudiologyLinguisticsMedicine

Abstract

fetched live from OpenAlex

This paper aimed to establish preliminary normative data scores of nasalance value for the Arab Emirati speakers, and to compare them to other Arabic speakers, particularly the Saudi, Jordanian and Egyptian speakers. Design was a cross-sectional study where nasality scores (nasalance percentages) were obtained under oral and vowel passage tasks. Participants were 104 Emirati individuals (54 males, age range 18-27, and 50 females, age range 18-27). Each participant was asked to extend the /a:/ vowel and read a passage in Arabic. A nasometer model II, 6450 (KayPentax, Canada) was utilized for nasalance scores computation. The ANOVA revealed no significant differences between the female and male Emirati speakers’ nasalance scores in both the vowel (males= 26.35, females= 23.3) and the oral passage tasks (males= 15, females= 15.1). The Emirati speakers had higher nasalance scores than the Saudi speakers in both tasks, and in the /a:/vowel task compared to the Egyptian and Jordanian speakers. Language and dialect are two important variables in determining the nasalance normative scores..

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)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.293
GPT teacher head0.451
Teacher spread0.159 · 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 designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

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