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Record W2993690901 · doi:10.60082/0829-3929.1367

“All Arabs Are Liars”: Arab and Muslim Stereotypes in Canadian Human Rights Law

2019· article· en· W2993690901 on OpenAlexvenueaboutno aff
Reem Bahdi

Bibliographic record

VenueJournal of Law and Social Policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsTerrorismRacializationConvictionTribunalPolitical scienceExistentialismCitizenshipIslamLawIslamophobiaSociologyCriminologyPoliticsHistory

Abstract

fetched live from OpenAlex

Stereotypes exclude, stigmatize, and burden Arabs and Muslims in Canada. This article examines three prevailing Arab and Muslim stereotypes: the conviction that Arabs and Muslims have a culturally ordained propensity towards violence; the belief that, regardless of their citizenship status, Arabs and Muslims remain foreigners who threaten Western values and; the notion that Arabs and Muslims are dishonest. The analysis rests on the facts found and conclusions reached in nine claims filed by Arab or Muslim applicants before the British Columbia, Ontario, Quebec or Canadian human rights tribunals. The tribunal decisions reveal that the terrorist profile requires the other two profiles for its efficacy, but the liar/untrustworthy motif and the un-Canadian/existential threat motif also operate independently of the terrorist motif. The cases also suggest that gender, racialization, and religion mediate the way in which the different stereotypes are invoked, and that Arabs and Muslims are stereotyped in diverse contexts including workplaces, schools, and state institutions. The cases also illuminate how stereotyping has profoundly impacted the financial, emotional, physical, and social health of the complainants but the human rights regimes examined do not always recognize the stereotypes that arise on the facts before them.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.348
Teacher spread0.313 · 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 designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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