Trojan Horse Technologies: Smuggling Criminal-Legal Logics into Healthcare Practice
Bibliographic record
Abstract
In the throes of an intractable overdose crisis, U.S. pharmacists have begun to engage in an unexpected practice—policing patients. Contemporary sociological theory does not explain why. Theories of professions and frontline work suggest professions closely guard jurisdictions and make decisions based on the logics of their own fields. Theories of criminal-legal expansion show that non-enforcement fields have become reoriented around crime over the past several decades, but past work largely focuses on macro-level consequences. This article uses the case of pharmacists and opioids to develop a micro-level theory of professional field reorientation around crime, the Trojan Horse Framework. Drawing on 118 longitudinal and cross-sectional interviews with pharmacists in six states, I reveal how the use of prescription drug monitoring programs (PDMPs)—surveillance technology designed for law enforcement but implemented in healthcare—in conjunction with a set of field conditions motivates pharmacists to police patients. PDMPs serve as Trojan horse technologies as their use shifts pharmacists’ routines, relationships with other professionals, and constructions of their professional roles. As a result, pharmacists route patients out of the healthcare system and leave them vulnerable to the criminal-legal system. The article concludes with policy recommendations and a discussion of future applications of the Trojan Horse Framework.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".