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Record W2044248466 · doi:10.1080/13602004.2015.1019730

We Are Not All the Same: Arab and Muslim Students Forging Their Own Campus Communities in a Post-9/11 America

2015· article· en· W2044248466 on OpenAlexaboutno aff
Diane Shammas

Bibliographic record

VenueJournal of Muslim Minority Affairs · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupFaithMuslim communityGender studiesEthnic communityQuarter (Canadian coin)SociologyIslamPolitical scienceGeographyAnthropologyTheology

Abstract

fetched live from OpenAlex

The study investigates Arab and Muslim students’ social relationships on 21 community college campuses in the USA (N = 753), with comparison groups of African Americans, Latinos, Asians, and Whites (N = 567). Survey findings revealed a positive relationship between campus friendships and sense of belonging, controlling for ethnic and religious identities, and perceived discrimination on campus. Individual group differences were found among Arab Christians, Arab Muslims, and non-Arab Muslims, and the comparison group. As an earlier publication based on the same study revealed, 75% of Arab and Muslim students’ campus friendships were either same ethnic and/or same faith, with only a quarter of campus friendships being of different ethnicity and different religion. Although such results suggest the existence of ethno-religious enclaves, this paper concludes that Arab and Muslim students are not purposely enacting their own ethno-religious balkanization, but—like other ethnic groups—forging their own campus communities within the larger campus community.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.077
GPT teacher head0.360
Teacher spread0.282 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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