‘This Is Not a Patient, This Is Property of the State’: Nursing, ethics, and the immigrant detention apparatus
Bibliographic record
Abstract
This paper opens with first-hand accounts of critical care medical interventions in which detainees, in the custody of U.S. Immigration and Customs Enforcement (ICE), are brought to the emergency department for treatment. This case dramatizes the extent to which the provision of ethical and acceptable nursing care is jeopardized by federal law enforcement paradigms. Drawing on the scholarship of Michel Foucault and Giorgio Agamben, this paper offers a theoretical account of the power dynamics that inform the health care of patients who find themselves caught in the custodial scaffolding of a vast immigration and detention apparatus. It offers an analysis of the display of sovereign and biopolitical power over the lives (and deaths) of detainees (Foucault), as well as the ways these individuals are reduced to "bare life" under the political pretext of an emergency or "state of exception" (Agamben). Our purpose here is both theoretical and practical: to better understand the often hidden agency or impersonal "will" exercised by the immigrant detention system, but also to equip clinicians in these and cognate facilities (e.g., prisons) with the critical tools by which they might better navigate incommensurable paradigms (i.e., care vs. custody) in order to deliver the best care while upholding their ethical duties as a care provider. This is all the more pressing because hospitals are not sanctuaries and given the incursion of federal law enforcement agents, nurses may find themselves conscripted as de facto agents of the state.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.073 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| 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 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".