Ensuring that people who use drugs are safely and equitably included at meetings and conferences
Bibliographic record
Abstract
Collaborating with people who use or used drugs (PWUD) in research and policy fora and incorporating their perspectives in decision-making processes is a crucial step towards mitigating drug policy-related harm. PWUD are experts in drug use and equipped to share their experiences and knowledge and impact drug policy change. However, equitable inclusion of PWUD in conferences and other fora is typically inadequate. PWUD were central participants in the planning and implementation of the Stimulus 2018: Drugs, Policy, and Practice conference. Conference planners made considerable effort to ensure PWUD attendees had their physical and emotional needs met, including access to overdose prevention services. However, guidance is needed to better safeguard the physical and mental health of conference attendees who use drugs. This Research & Practice Note aims to initiate discussion on this underexplored topic and provide ideas for the safer inclusion of PWUD within research and policy fora.
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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 | 0.079 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".