Hate Crime Prevention Strategy of Canada and Political Implications: Focusing on a multicultural perspective
Bibliographic record
Abstract
As the foundation of the nation's existence and a springboard for growth, this study analyzed the case of Canada, which recognizes a multicultural society and is actively pursuing related policies by establishing various legal and institutional foundations to suppress hate crimes, and through the foregoing, this study intended to explore policy measures from a multicultural perspective to suppress hate crimes in our society. Research results show that hate crimes have risen sharply in Canada due to the social unrest and fear caused by the COVID-19 pandemic and especially, hate crimes motivated by race and ethnicity have accounted for the highest proportion in the past decade. Furthermore, hate crimes motivated by religion and sexual identity have followed suit, suggesting a pattern very similar to that seen in Korean society. However, Canada is actively responding to hate crimes from a multicultural perspective through various legal grounds such as the「Criminal Code」,「Canadian Human Rights Act」 and「Canadian Multiculturalism Act」, and it has been revealed that it is systematically pursuing a step-by-step policy to suppress hate crimes through ‘Canada’s Action Plan on Combatting Hate’ established in 2024. In addition, in Canada, it was revealed that preventive education to suppress hate crimes from a multicultural perspective is being strengthened, especially in the education sector, through laws and institutional devices such as the Accepting Schools Act in Ontario and the Multicultural Advisory Council (MAC) and the Provincial Committee on Anti-Racism (PCAR) in British Columbia. Therefore, Korea, which is rapidly transforming into a multicultural society, should also follow the example of Canada and first establish a clear legal basis for suppressing hate crimes from a multicultural perspective and ensure strict punishment. Second, official statistics on hate crimes from a multicultural perspective should be published to clearly understand the current situation, and Third, a comprehensive and specific plan should be established to suppress hate crimes from a multicultural perspective. Fourth, simultaneously with expansion of preventive education to promote understanding of multicultural society, especially in the educational sector, and to suppress discrimination and prejudice against them, Fifth, Korea must respond strictly to rallies and demonstrations and mass media that promote various hate crimes that run counter to multicultural society to ensure that active regulation and punishment are implemented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| 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".