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Record W4293716744 · doi:10.5430/wjel.v12n6p343

Social Attitudes Towards Bedouin and Sedentary Dialects in Central Najd

2022· article· en· W4293716744 on OpenAlexvenueno aff
Nasser Mohammed Alajmi

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityCivilizationPsychologyTest (biology)DemographyGeographySociologyPolitical science

Abstract

fetched live from OpenAlex

This study examines whether Bedouins in Najd are converging on the sedentary dialect due to social attitudes towards these two dialects. It is hypothesized that the social attitudes towards the sedentary dialect will be positive, as opposed to those of the bedouin dialect. The social attitudes towards the dialects will be measured using indirect approach, via the verbal-guise test. In this test, the implicit attitudes of individuals towards a language/dialect are measured. Participants are presented with stimuli (short speech excerpts) from both dialects and asked to rate each speaker on a list of selected status and solidarity traits. The results show that the sedentary dialect ranked higher than bedouin dialect in civilization, education, open-mindedness, and expressing emotions. The bedouin dialect, however, ranked higher only in self-confidence. Thus, it is stated that the bedouins are converging on the sedentary dialect because of Najdis’ social attitudes towards the sedentary dialect.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.308
Teacher spread0.292 · 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
Published2022
Admission routes1
Has abstractyes

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