“Absolutely the worst drug I’ve ever seen”: Risk, governance, and the construction of the illicit fentanyl “crisis”
Bibliographic record
Abstract
In this article, we analyze 1027 articles published in four newspapers in order to trace the construction of the fentanyl “crisis” across social contexts. Our analysis reveals that Chinese producers and Mexican cartels were censured for bringing this deadly substance into Canada and the United States as the number of fentanyl-related deaths and overdoses increased. Indeed, news media construct this “illicit” form of fentanyl as foreign and risky. We contend that this coverage diverts attention away from the consequences of the neoliberal policies that contribute to opioid use and plays an important role in stoking feelings of insecurity that justify a disconcertingly wide range of governing practices that aim to secure the homeland against external threats, advance the state’s interests abroad, and discipline larger swaths of the population at home.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".