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Record W2526888705 · doi:10.53956/jfde.2016.78

Muslim Female Students Confront Islamophobia: Negotiating Identities In-between Family, Schooling, and the Mass Media

2016· article· en· W2526888705 on OpenAlexaffabout
Diane P. Watt

Bibliographic record

VenueJournal of Family Diversity in Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCurriculumIslamophobiaMulticulturalismSociologyPedagogyNegotiationIdentity negotiationIdentity (music)Gender studiesMulticultural educationMass mediaPolitical scienceSocial sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Abstract: This article researches how Muslim students in Canada negotiate identity in an extremely complex discursive terrain of the unofficial Islamophobia curriculum of family, schooling, and mass media. Critical examination of the exclusion of Muslims from school policies and the absence of Muslim experiences and perspectives in the Ontario Language Curriculum are highlighted. This article aims at developing teacher educators, in-service teachers and teacher candidates’ critical multicultural awareness of how Muslim minority students negotiate the absence of their culture in the secondary language curricula. Drawing from postcolonial feminist perspectives and curriculum theory this research was conducted with seven young Muslim women as participants. Findings indicate while absent in the official secondary language curriculum, the unofficial curriculum represents Muslim women as the cultural “other” sustained through the unofficial school curriculum and media portrayals. This study argues for a need to involve teacher educators, in-service teachers and teacher candidates in complicated conversations on cultural and linguistic differences, engagement with life-experiences of cultural minorities, development of complex pedagogies, critical media literacies and multicultural practices that are diverse and inclusive.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.345
Teacher spread0.300 · 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 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

Citations9
Published2016
Admission routes2
Has abstractyes

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