A Snapshot of Hate: Subjective Psychological Distress After a Hate Crime: An Exploratory Study on Victimization of Muslims in Canada
Bibliographic record
Abstract
Across Canada, hate crimes, especially those motivated by race, ethnicity, or religion, are still prevalent. For example, in 2019, 46% of police-reported hate crimes were motivated by race or ethnicity, and 32% were motivated by religion (Moreau, 2021). In Canada, Muslims are the second most targeted religious group in terms of hate crimes. However, Canadian research on the nature of hate crime victimization amongst Muslims and the impacts on their health and well-being is limited. The present study sought to use exploratory survey data to assess the demographic characteristics of those experiencing both verbal and physical assaults based on their religion. Further, we assessed whether those that experienced these assaults also experienced psychological distress (such as feeling nervous or hopeless). Based on a sample of 230 participants (58% women), it was found that individuals that self-identified as visibly Muslim were 3 times more likely, and those living in Vancouver were 9 times more likely, to report having been physically assaulted. Furthermore, having been physically assaulted, being a woman, residing in Vancouver, or self-identifying as visibly Muslim were factors associated with higher levels of psychological distress. This study is the first of its kind exploring the effects of hate crimes on Muslims across Canada. The impacts of hate crime on the psychological well-being of this marginalized population, especially for Muslim women, suggests a need for more research on the psychological distress of these individuals
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".