“All Arabs Are Liars”: Arab and Muslim Stereotypes in Canadian Human Rights Law
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.040 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".