Disrupting and Maintaining Prohibition: Institutional and Grassroots Harm Reduction Practices in Ottawa
Bibliographic record
Abstract
Harm reduction is often proposed to be a unique response from prohibition.Where prohibition restricts and punishes most activities related to drugs, the term "harm reduction" is used to denote a number of provisions (e.g.equipment for using, a place to use).In this dissertation I trouble this distinction.I argue that the relationship between harm reduction and prohibition is more dynamic.Rather than operating in separate silos, harm reduction is actively constructed by, while also shaping and influencing, prohibition.For this research I conducted participant observations at two harm reduction services in Ottawa, Canada.The first service is operated by a community health centre, and the second is provided by a grassroots community organization.Engaging in the provision of harm reduction at these two sites demonstrated that practices of harm reduction and prohibition shape and influence each other.In particular I found that harm reduction and prohibition are connected through their legality, including the formal and quotidian ways that our actions are guided by and produced as legal.I use a multipronged critical legal studies framework to draw out these various ways that law is produced and experienced.In specific I demonstrate that the relationship between harm reduction and prohibition is a product of: grassroots efforts to disrupt prohibition and legal and political efforts to maintain it; spatial and temporal relations; and exclusionary strategies of control.Examining the relationship between harm reduction and prohibition provides insight into the potential for and barriers to efforts that could address the harms people who use drugs experience on a day-to-day basis, including the threat of criminalization and fatal overdose.iii Acknowledgements I am so deeply grateful to all of the people who shared a part of their life with me, included me in the harm reduction community in Ottawa, and allowed me to engage in transformative, direct action with them.Your resilience, strength, compassion, and the experiences we shared continue to motivate me to advocate for drug policy reform.Getting the opportunity to conduct this research is a privilege I hold close to my heart.From my heart to yours, words cannot express the appreciation and respect I have for everyone who made this project possible!The path to completing this dissertation was long and winding.I could not have finished it without the guidance and supervision of Dr. Stacy Douglas.Stacy, you were attune to the support I needed and helped me find my way forward.You listened to me, distilled my arguments, and helped me sculpt this project.I cannot thank you enough for providing me the opportunity to forward this work on my own terms.To my committee members, Dr. Marie-Ève Sylvestre and Dr. Dale Spencer, who travelled this path along with me, thank you! Marie-Ève, I am so grateful that you invested in this project and in me.Your guidance and approach to teaching enabled me to expand my comprehension of law, and continues to push me to go further
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".