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Record W2591936665 · doi:10.1057/978-1-137-56921-9_10

Islamophobia in Quebec Secondary Schools: Inquiries into the Experiences of Muslim Male Youth Post-9/11

2017· book-chapter· en· W2591936665 on OpenAlexaffabout
Naved Bakali

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcGill University
Fundersnot available
KeywordsIslamophobiaPolitical scienceGender studiesSociologyLawPolitics

Abstract

fetched live from OpenAlex

In this chapter, Bakali provides a timely examination of the presence of Islamophobia in Quebec secondary schools in the post-9/11 context. Employing a critical ethnographic approach stemming from institutional ethnography, this chapter explores systemic and institutional racism experienced by young Muslim men in their secondary schools and possible causes for this treatment. Through engaging with participants in individual interviews and focus group discussions, Bakali describes how participants regularly encountered bias from classmates and teachers relating their perceived faith. The findings in this chapter suggest that anti-Muslim racism experienced by participants was inextricably linked to the effects of the War on Terror in the North American context. Moreover, these experiences were also impacted by Quebec state practices, policies, and political and media discourses. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
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 score0.184
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0260.008
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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