Minimizing and Denying Racial Violence: Insights from the Québec Mosque Shooting
Bibliographic record
Abstract
On 29 January 2017, a twenty-seven-year-old white man named Alexandre Bissonette entered a mosque in a suburb of Québec City and opened fire, killing six people. Focusing on Canadian media reports, this article examines two seemingly incongruent responses to this heinous massacre. First, despite Bissonnette’s unambiguous and purposeful targeting of Muslims, the public and the courts still debated whether this massacre was racially motivated. Second, when members of the Muslim community commented on the massacre and the impact that it had had on them, there appeared to be a type of restraint in the ways in which they expressed their fears and frustrations and in the ways in which they addressed the issue of anti-Muslim racism. How do we understand these incongruences? This article draws upon Sherene Razack’s seminal scholarship on public grief, national mythologies, and anti-Muslim racism in Canada, alongside studies on public expressions of emotions to make sense of the role that race played in the responses by the Muslim community, the politicians, the courts, and the accused.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.053 | 0.022 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 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".