“It's such a terrible drug”: Narratives of fentanyl dealers amid the opioid overdose crisis
Bibliographic record
Abstract
Abstract The fentanyl‐fueled overdose crisis is historically lethal, yet the voices of those who sell fentanyl remain understudied. While research has focused extensively on people who use drugs (PWUD), the perspectives of people who sell fentanyl (PWSF) are largely absent from academic and policy discussions. This study draws on 87 in‐depth interviews with incarcerated individuals in Western Canada who have experience using and selling fentanyl. Using a narrative criminological approach, we allowed participants’ stories to guide the interviews, exploring how they interpret their actions, identities, and harm. Thematic coding revealed how PWSF navigate tensions between control, responsibility, and victimhood as they attempt to morally frame or neutralize their role in distributing a deadly substance. Our findings show that fentanyl's extreme lethality complicates traditional neutralization techniques, amplifying feelings of moral and legal accountability. Compared to other people who sell drugs (PWSD), PWSF demonstrate three distinct characteristics: stronger harm reduction practices, heightened moral awareness, and greater acceptance of legal consequences. This research sheds light on the complex realities of fentanyl distribution, emphasizing the need for harm reduction and criminal justice responses that consider the ethical and structural dimensions shaping the actions of low‐level sellers.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| 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".