Bibliographic record
Abstract
The first week of February 2014 saw the tragic deaths of two young people in Québec: Naïma Rharouity, a Muslim woman and mother of two who died following an accident in a metro station and Alain Magloire, a Black man and father of two killed by the Montreal police. Muslim women and Black men are racialized within Québec society in significantly different ways from one another, in life as in death. This article analyzes the reactions to and representations of these two deaths in the specific context of Québec and how they fit into heavily racialized scripts. A Muslim woman is strangled to death when an escalator catches her clothing; her hijab is blamed, making her a victim of her culture and dead because of the scarf she wore on her head. A Black man holding a hammer outside a metro station is deemed as so dangerous, violent, and threatening by armed police officers that he is shot dead. Both were victimized but also blamed for their untimely deaths. The challenges these stories pose disrupt assumptions and demand alternative narratives about racialized bodies. This article reveals the different processes of racialization of Muslim women and Black men, and argues that exposing the internal logic of this comparison promotes critical understanding of the ways in which racialized scripts shape and influence our lives. This further highlights ways to work towards building stronger solidarities to resist and challenge narratives that demand tragic endings for racialized bodies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.035 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".