Bibliographic record
Abstract
Safe injection sites have become the next battlefield in the conflict between state and federal drug laws. A safe injection site is a place where injection drug users can self-administer drugs in a controlled environment under medical supervision. They have been operating in other countries, including Canada, for decades, and a wealth of evidence suggests that they can help to reduce overdose deaths. To date, however, no United States city or state has sanctioned a safe injection site. Until recently, safe injection sites were politically untenable, seen as a form of surrender in the war on drugs. This dynamic, however, has changed over the past few years. Prominent politicians from across the political spectrum have called for an end to the drug war, and the opioid epidemic has grown increasingly dire. Efforts to start safe injection sites are currently underway in at least thirteen United States cities and states. Five cities—Denver, New York, Philadelphia, San Francisco, and Seattle—have gone so far as to announce plans to open an injection site. There is just one small problem: the site proposals appear to violate the federal crack house statute, which makes it a crime to maintain drug-involved premises. The Department of Justice has not yet taken a formal position on safe injection sites, but in a New York Times editorial, Deputy Attorney General Rod Rosenstein threatened that “cities and states should expect the Department of Justice to meet the opening of any injection site with swift and aggressive action.” Surprisingly, this looming conflict has gone almost entirely overlooked by legal academics. Meanwhile, the public debate has assumed that safe injection sites are clearly forbidden by federal law. This Article argues that assumption is wrong. Despite the crack house statute, an obscure provision of the federal Controlled Substances Act (CSA) might allow states and localities to establish government-run safe injection sites. Buried in the CSA is a provision that immunizes state and local officials who violate federal drug laws in the course of “the enforcement of any law or municipal ordinance relating to controlled substances.” This provision was almost surely intended to protect state and local police officers that possess and distribute drugs in connection with undercover operations. Nevertheless, this Article argues, the text of the immunity provision and the scarce case law interpreting it suggest it could shield government-run safe injection sites from federal interference.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.038 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".