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Record W4409845626 · doi:10.1177/14648849251338238

Bringing situated knowledges, Islamic values, and identity into the newsroom: Muslim female journalists in Canadian media and counter-narratives to Islamophobic discourse

2025· article· en· W4409845626 on OpenAlexafffundabout
Hafsa Maqsood

Bibliographic record

VenueJournalism · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIslamSituatedIslamophobiaNarrativeIdentity (music)Gender studiesSociologyMedia studiesPolitical scienceLinguisticsTheologyArtPhilosophyAestheticsComputer science

Abstract

fetched live from OpenAlex

This paper explores the experiences of Muslim women in Canadian journalism. Through a critical discourse analysis of qualitative interviews with eight Muslim female journalists, this research elucidates their experiences with a gendered-religious nuance considering the specificities of gendered Islamophobia. It further moves past the established discourse of racialized women in media facing burdens of representation to examine how they unite their lived experience as Muslim women with journalism epistemology to challenge gendered Islamophobic discourses and unite Islamic values with journalistic practice. Drawing on theoretical frameworks like critical race theory and Orientalism, this research ultimately finds that the women challenge dominant discourses of objectivity in journalism and Islamophobic discourses surrounding Muslim women. They accomplish this by drawing on Islamic principles in their professional practice, ultimately contesting a key tenet of Islamophobic, Orientalizing discourse that establishes Islam as antithetical to Western values and Muslims as unable to integrate into increasingly secular Western 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.513
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.351
Teacher spread0.337 · 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 teacher head, 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

Citations0
Published2025
Admission routes3
Has abstractyes

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