Perspectives of Muslim and Minority Canadian Youth on Hate Speech and Social Media
Bibliographic record
Abstract
In this article, we highlight the perspectives of marginalized Canadian youth regarding hate speech on social media. Specifically, our research focus is on the complexity and intersectionality involved in cyber violence, especially in relation to marginalized identities. Twenty-five participants aged 18 to 25 studying at a central Canadian University (from an initial sample of 90 participants) who self-identified as victims of hate speech were invited to share their experiences and narrate their stories. Research results demonstrate that online hate speech is growing in Canada to an extent where it is has become normalized. This has serious implications for the well-being of Canadian youth - both perpetrators and victims of hate speech. The main targets of hate speech on social media in Canada are immigrants and minorities, particularly Muslims. Results show that online hate speech has significant consequences for the lives of Canadian youth. The repercussions for the victim's mental and physical well-being manifest in problems ranging from alienation, identity issues, deterioration of psychological and physical health to cyber and in-person bullying, and much more. The study concludes that while there are definite links between the rise of online hate speech, deterioration of mental and physical health, and increased attacks on immigrants and minorities, not much action has gone into policymaking and education to correct the situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".