How Structural Violence, Prohibition, and Stigma Have Paralyzed North American Responses to Opioid Overdose
Bibliographic record
Abstract
As of 2020, North America is now into the fifth year of an unprecedented increase in drug overdose deaths driven by a toxic, unpredictable, and unregulated drug supply. While the genesis and drivers of and response to the opioid overdose crisis have wide regional variations, structural violence, prohibitions against illicit drug use, and stigma consistently play a central role. The criminalization of users of illicit drugs has led directly not only to users' incarceration, but also to their marginalization and isolation and to violence, entrenched poverty, and a vicious cycle of trauma. This policy has created an environment wherein any initiatives to prevent and reverse overdoses have been severely restricted. While a war on drugs and the people who use them has been widely criticized as destructive and unwinnable, the criminal policies that support the war on drugs have not changed even in response to this unprecedented crisis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".