Medicalized Discourses and Injection Drug Use: Ascribing Humanity or Autonomy?
Bibliographic record
Abstract
In this paper, I draw on interviews conducted with people associated with Insite, North America’s first and only supervised injection facility located in Vancouver, Canada, to consider the relationship between dependence, autonomy, and vulnerability. At Insite, a government funded facility, injection drug users can consume pre-obtained illegal drugs under medical supervision. I argue that by conceiving of Insite as a healthcare facility and addiction as a disease, advocates of Insite have evoked a shared notion of vulnerability among the non-drug using public and drug users, which has garnered considerable public support for the controversial site. Through Insite, the state has responded to vulnerability by providing services that reshape the meaning of dependence and in turn promote resilience for vulnerable human beings. However, the medicalized discourse that has been instrumental in cultivating support for Insite may also obscure the vulnerabilities associated with drug dependence that emerge not from addiction as “disease,” but from the stigmatization and criminalization of drug use. In turn, I argue that we should be aware of the potential pitfalls of making political claims based on a naturalized conception of vulnerability. I emphasize the socially constructed nature of human beings’ experiences of vulnerability. In turn, I argue that a state that is responsive to vulnerability must participate in the restructuring of the social relations that give meaning to notions of dependency and vulnerability themselves. In doing so, the state also actively fosters autonomy in marginalized citizens.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".