MétaCan
Menu
Back to cohort
Record W4403929591 · doi:10.5539/ijel.v14n6p122

Linguistic Convergence and Divergence in the Use of the Third Person Singular Feminine Suffix among the Qassimi Speech Community in Riyadh

2024· article· en· W4403929591 on OpenAlexvenueno aff
Shahad Mohammed Almayouf

Bibliographic record

VenueInternational Journal of English Linguistics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsDivergence (linguistics)Convergence (economics)SuffixLinguisticsMathematicsPsychologyEconomicsPhilosophy

Abstract

fetched live from OpenAlex

This study examines the linguistic impact of dialect contact on Qassimi Arabic (QA) speakers residing in Riyadh. Focusing on the morphophonemic feature of the third-person singular feminine suffix (-ah/-ha), the research investigates patterns of convergence and divergence through a variationist sociolinguistic lens. Data from sociolinguistic interviews with 32 QA speakers in Riyadh were analyzed using SPSS. Findings indicate a prevalent use of the non-standard variant [-ah], suggesting a strong preservation of traditional linguistic features among QA speakers despite exposure to other Saudi dialects. This was explained by the concentration of QA speakers in particular neighborhoods creating ethnic enclaves that restrict exposure to other Saudi dialects and thus preserving pre-migration speech patterns. The suffix’s morphophonemic function and lack of social or symbolic meaning also had a role in its preservation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.405
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicMultilingual Education and PolicyFrench-language works237,207