Exploring Subjectivity Through Safer Supply
Bibliographic record
Abstract
In 2019, the Canadian government approved the prescription of medical heroin to people with severe opioid use disorder, not as a treatment for addiction, but as a harm reduction tactic to prevent overdose in the face of an increasingly toxic street drug supply (Health Canada 2019). This harm reduction tactic is known as safer supply. Since 2019, the Canadian government has broadened the scope of safer supply to allow physicians to prescribe opioids and other drugs that they deem necessary for their patients (Health Canada 2023). In this thesis, I argue that the regulatory flexibility of safer supply allows physicians to meet their patients where they are, instead of asking them to ascribe to social norms to access care. Safer supply does not seek to end drug use; it only seeks to prevent overdose. Within the clinic, people who use drugs find they are able to understand their drug use through a positive medical/functional model instead of the criminalized model of addiction; many no longer understand their use of opioids to be categorized as addiction at all. Through focusing on personal narratives of addiction and drug use, I argue that many of the harms considered inherent to opioid use within liberal frameworks are caused by the structural violence inherent in the punitive criminalization of drug use. Safer supply allows people to form less pathologized and stigmatized subjectivities of drug use because it does not presume all drug use to be inherently problematic and removes many of the aspects of drug use that people who use drugs find harmful. Accessing safer supply is an act of care of the self and it allows people the space in their lives to begin ethical work. It is critical that as governments develop policy solutions, they consider the voices of people who will use those solutions.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.106 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".