Bibliographic record
Abstract
The intent of this chapter is to contextualise research findings about substance use by professionals within contemporary political, legal, and societal contexts, with a focus on decriminalisation. Decriminalisation tends to be framed in relation to populations most vulnerable to harms associated with substance use and criminalisation, while relevance to the broader public remains largely unexamined. From 2017 to 2019, my team undertook research on substance use among professionals in Canada, a population which benefits from protective factors that mitigate harm. We found that substance use is not uncommon among professionals and there is a high degree of choice and control. Among this population, non-disclosure of substance use is the norm, due to potential social repercussions and the involvement of regulatory bodies. While our findings demonstrate predominantly beneficial and non-problematic experiences of substance use, such experiences remain largely absent from social, legal, and political discussions. To contextualise our findings within current political, legal, and societal contexts, I examine current deliberations about harm in relation to (de)criminalisation; namely harm of substances and harm of drug laws . Concerns around personal and societal harms arising from the effects of substance use, cultivation, and distribution coexist alongside concerns of harm as an outcome of drug law and policy enforcement. Evaluations of harm that pertain to both substance use and drug laws need to be more firmly conceptualised to better ascertain the effectiveness of national and international policies, laws, and conventions. To facilitate understanding about non-problematic and beneficial experiences of substance use, researchers, policy-makers and legislators need to create safe opportunities for personal disclosure across all members of society.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".