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Record W4377024471 · doi:10.5430/wjel.v13n6p151

Saudi College Students' Arabic & English Reading Attitudes

2023· article· en· W4377024471 on OpenAlexvenueno aff
Hamad Mohammed Alluhaydan

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PleasureArabicPsychologySocial psychologyMathematics educationLinguistics

Abstract

fetched live from OpenAlex

The study examined Saudi college students' Arabic and English reading attitudes and considered the influence of gender on reading attitudes. A correlational quantitative approach was used to study 115 participants' reading attitudes in both languages. Participants displayed uncertain attitudes toward Arabic reading practice, and more positive attitudes towards English reading. Females demonstrated more positive attitudes towards reading practice in both languages than males. Females' social communities had less positive reading attitudes toward Arabic and English reading than males’ social communities. In the absence of family encouragement to practice pleasure reading at homes, students did not read widely. Siblings’, friends’, and peers’ choice of books highly influenced participants' choices of reading materials. Study participants reported that Saudi schools, especially males’ schools, did not profoundly impact on their reading attitudes. Pleasure reading was not encouraged. Neither were students taught how to find books that suited their interests or encouraged to spend time in the schools' library.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.025
GPT teacher head0.354
Teacher spread0.330 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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