Rising above hate: Policy options to address hate crimes and hate incidents in British Columbia
Bibliographic record
Abstract
Canada is a diverse country with 22.3% of Canadians belonging to visible minorities (Wang & Moreau, 2022). Canada’s history has been marked by systemic racism against visible minorities such as Indigenous, Black, and Asian communities. The onset of the COVID-19 pandemic, however, brought racism into stark relief with an increase in the number of hate crimes and hate incidents across Canada. This study examines the reasons behind the prevalence of hate crimes and hate incidents against visible minorities in the province of British Columbia. The study aims to better understand the experiences of racism in the province of British Columbia through focus group discussions involving members of the racialized communities, and also by drawing on evidence derived from secondary sources to understand the reasons behind the issue. Interviews were conducted with key informants belonging to various organizations involved in efforts to combat racism to determine potential gaps in current policies and government initiatives. The compilation and analysis of the data obtained in the study identified several key factors that contribute to the problem of hate crimes and hate incidents. Four policy options comprised of short to long term solutions have been proposed to address the issue of hate crimes and hate incidents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".