Bring it on Home: Home Drug Testing and the Relocation of the War on Drugs
Bibliographic record
Abstract
While the war on drugs is often claimed to have failed in multifarious ways, anti-drug strategies in the United States continue. The discourses through which anti-drug sentiments and policies are forwarded are, however, being reinvented in light of this failure, favoring an inclusionary and less state-centered disease trope for certain populations of drug users. In this article we argue that the privileging of the disease trope within anti-drug rhetoric facilitates the introduction of home drug testing as a means of 'state-free' drug regulation offered to specific populations. The advent of home drug testing is congruent with neoliberal trends towards mobilizing private entities like the family to engage in regulatory practices that were previously concerns of the state. A market for home drug testing has evolved out of rhetoric around private security, and the commodification of notions of safety. Home drug testing is theorized as a tool of surveillance that offers a very particular scientific gaze trained on the seemingly indefensible adolescent body. Teens, however, are not defenseless in this scheme. We document the concomitant rise of resistance technologies and tactics designed to assist teens and others to 'beat' the tests.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".