Understanding conventional and novel approaches used to advance evidence-based illicit drug policy
Bibliographic record
Abstract
A considerable amount of research has shown traditional illicit drug policies represent a critical source of inequity and ongoing health-related harms on a global scale. The harms associated with these policies have spurred several calls for “evidence-based” policy reform whereby policies that criminalize drug users be replaced with public health approaches. These calls for policy reform, and the persistence of criminal justice based approaches, have raised questions about the strategies and tools scientists, researchers, academics and/or health practitioners may mobilize to support this objective (herein referred to collectively as scientists). In this context, the primary objectives of this thesis were to: 1) synthesize what is known about conventional activities and strategies scientists use to advance evidence-based drug policies and 2) to describe and evaluate in detail the Vienna Declaration campaign, the largest scientist-led mobilization to support evidence-based illicit drug policy to date, and 3) to generate insights into strategies that may support the advancement of evidence-based illicit drug policy, especially as they related to public and political discourse. This work reveals scientist-led efforts to promote evidence-based drug policy have not traditionally made use of the Internet and related tools. Findings from an analysis of the Vienna Declaration campaign reveal that the Internet and social media are important dissemination tools that support science-based efforts to advance evidence-based drug policy. Given the deficit of research in this area and long-standing limitations to scientists’ proficiency engaging the public, media, and policymakers, the thesis concludes additional research is needed to better understand the tools and strategies available to scientists working in this area. It speculates that such a research agenda may also serve as a culturally appropriate way of engaging scientists and influencing their future knowledge translation efforts.
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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 | 0.146 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.041 | 0.041 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".