Between Care and Control: Examining Surveillance Practices in Harm Reduction
Bibliographic record
Abstract
As harm reduction programs and services proliferate, people who use drugs (PWUD) are increasingly subjected to surveillance through the collection of their personal information, systematic observation, and other means. The data generated from these practices are frequently repurposed across various institutional sites for clinical, evaluative, epidemiological, and administrative uses. Rationales provided for increased surveillance include the more effective provision of care, service optimization, risk stratification, and efficiency in resource allocation. With this in mind, our reflective essay draws on empirical analysis of work within harm reduction services and movements to reflect critically on the impacts and implications of surveillance expansion. While we argue that many surveillance practices are not inherently problematic or harmful, the unchecked expansion of surveillance under a banner of health and harm reduction may contribute to decreased uptake of services, rationing and conditionalities tied to service access, the potential deepening of health disparities amongst some PWUD, and an overlay of health and criminal-legal systems. In this context, surveillance relies on the enlistment of a range of therapeutic actors and reflects the permeable boundary between care and control. We thus call for a broader critical dialogue within harm reduction on the problems and potential impacts posed by surveillance in service settings, the end to data sharing of health information with law enforcement and other criminal legal actors, and deference to the stated need among PWUD for meaningful anonymity when accessing harm reduction and health services.
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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 | 0.165 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".