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Record W2746831794 · doi:10.1080/13613324.2020.1798388

Impact of Islamophobia on post-secondary Muslim students attending Ontario universities

2020· article· en· W2746831794 on OpenAlexaffabout
Hassina Alizai

Bibliographic record

VenueRace Ethnicity and Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsQueen's University
Fundersnot available
KeywordsIslamophobiaTerrorismIdentity (music)Context (archaeology)Gender studiesSociologyPolitical scienceInterviewRacismIslamHigher educationPoliticsLawTheology

Abstract

fetched live from OpenAlex

This study investigated the experiences of Muslim students attending Canadian institutions of higher education in the context of increasing Islamophobia. Qualitative semi-structured interviewing was used to explore the impact of anti-Muslim sentiment on the academic experiences of Muslim students and patterns of identity construction subsequent to recent national and international terrorism-labeled events such as the 2015 Paris bombings and 2015 San Bernardino mass shooting. Analysis of interview data yielded two major themes: (a) the formation of a strong religious identity in response to experiences of Islamophobia and (b) a distinction between general Islamophobia and gendered Islamophobia. The findings suggest that Muslim students in the current post 9/11 era are becoming increasingly devout, have a strong attachment to their religious identity, and are at the forefront of advocating for Muslims through education, activism, civic participation, and interfaith dialogue.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.886

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 studies0.0110.004
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.030
GPT teacher head0.362
Teacher spread0.331 · 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

Citations36
Published2020
Admission routes2
Has abstractyes

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