“The machine doesn't judge”: Counternarratives on surveillance among people accessing a safer opioid supply via biometric machines
Bibliographic record
Abstract
People who use illegal drugs experience routine surveillance, including in healthcare and harm reduction settings. The MySafe Project - a safer supply pilot project that dispenses prescription opioids via a biometric vending machine - exists in the Canadian province of British Columbia. The machine scans a participant's palmprint and has a built-in camera that records every machine interaction. The aim of this paper is to understand participants' experiences of surveillance, privacy, and personal security when accessing this novel program. An integrative case study and grounded theory methodology was employed. Qualitative one-to-one interviews were conducted with 46 MySafe participants across three different program sites in Vancouver. We used a team-based approach to code interview transcripts and utilized directed and conventional content analyses for deductive and inductive analyses. While participants described negative experiences of surveillance in other public and harm reduction settings, they did not have concerns regarding cameras, collection of personal information, tracking, nor staff issues associated with MySafe. Similarly, while some participants had privacy concerns in other settings, very few privacy and confidentiality concerns were expressed regarding accessing the machine in front of others. Lastly, while some participants reported being targeted by others when accessing the machines, most participants described how cameras, staff, and machine locations helped ensure a sense of safety. Despite negative experiences of surveillance and privacy issues elsewhere, participants largely lacked concern regarding the MySafe program and machines. The machine-human interaction was characterized as different than some human-human interactions as the machine is completing tasks in a manner that is acceptable and comfortable to participants, leading to a social preference toward the machines in comparison to other surveilled means of accessing medications. These findings provide an opportunity to rethink how we conceptualize surveillance, medication access, and harm reduction programs targeting people who use drugs.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".