Ignored Inequities: The Case of British Columbia’s “Stop Overdose” Anti-Stigma Campaign
Bibliographic record
Abstract
Anti-stigma campaigns have become a common intervention for addressing the drug toxicity crisis in Canada. Recent reviews have shown a widespread trend where White, middle-class people who use drugs dominate the imagery and messaging of these campaigns, excluding marginalized people who use drugs who face disproportionate effects of substance use stigma. The current study investigates this troubling trend by examining the development process of the BC Government’s “Stop Overdose” anti-stigma campaign. Developers’ goal of shifting the focus away from “stereotypical” marginalized people who use drugs, the uncritical channeling of narratives about substance use, and the prioritization of both “relevance” and marketing-based knowledge may explain the campaign’s counterintuitive focus on White, middle-class people who use drugs. In effect, these strategies, goals, and priorities obscure the experience of marginalized people who use drugs, naturalizing deep inequities which perpetuate the drug toxicity crisis. This study highlights a need for further research on anti-stigma campaigns, especially as government organizations continue to allocate significant resources to their development.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.051 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".