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Record W51619896 · doi:10.20355/c5mw2m

Learning and Cultural Experiences of Arab Muslim Graduate Students in a Canadian University

2006· article· en· W51619896 on OpenAlexaffvenueabout
Gamal Mostafa

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIslamMulticulturalismAsset (computer security)Muslim communityGraduate studentsSociologyPost graduatePedagogyPolitical scienceGender studiesMedical educationTheologyMedicine

Abstract

fetched live from OpenAlex

The purpose of the present study is to explore the experiences of Arab Muslim graduate students in the University of Alberta (U of A) regarding cultural differences and adjustment, language difficulties, supervision, differences of study system, and funding. Semi-structured interviews were conducted with five Arab Muslim graduate students from Egypt, Libya, Oman, Iraq and Kuwait. The findings of the study reveal that although students experience some difficulties adjusting to Canadian society, adjustment is made easier through their interaction with local Muslim communities and organizations such as mosques. In addition, respondents indicate the multicultural nature of Canadian society is an asset that helps them o-n and off-campus. Moreover, the presence of Arab Muslim students at the U of A may help in correcting the image of Islam as a religion, distorted in the west, through interaction both academically and socially with other students. The study recommends greater cooperation between the university and Muslim cultural and religious organizations within Canadian society.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.680

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0200.008
Scholarly communication0.0050.001
Open science0.0020.004
Research integrity0.0010.003
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 head0.031
GPT teacher head0.366
Teacher spread0.334 · 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 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

Citations21
Published2006
Admission routes3
Has abstractyes

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