How can Community-Based Participatory Research Address Hate Crimes and Incidents?
Bibliographic record
Abstract
Reports of hate crimes in Canada have increased by 72% from 2019 to 2021 (Moreau, 2022). Hate crimes harm those directly victimized and members of targeted communities (Erentzen & Schuller, 2020; Perry & Alvi, 2011). Many Canadian stakeholders advocate for increased community engagement in preventative and responsive interventions to this increasing concern. This article poses that Community-Based Participatory Research (CBPR) is an appropriate approach for further exploring hate crimes and incidents and suggests strategies for this area of study, including: building community partnerships; advocating for trauma-informed practices; prioritizing cultural humility and intersectionality; preparing for lengthy pre-participation communication with potential participants; anticipating out-of-scope volunteer participants; and accounting for unanticipated actions of participants.
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 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 | 0.292 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.029 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".