Killing for the state: the darkest side of American nursing
Bibliographic record
Abstract
The aim of this article is to bring to the attention of the international nursing community the discrepancy between a pervasive 'caring' nursing discourse and a most unethical nursing practice in the United States. In this article, we present a duality: the conflict in American prisons between nursing ethics and the killing machinery. The US penal system is a setting in which trained healthcare personnel practice the extermination of life. We look upon the sanitization of deathwork as an application of healthcare professionals' skills and knowledge and their appropriation by the state to serve its ends. A review of the states' death penalty statutes shows that healthcare workers are involved in the capital punishment process and shielded by American laws (and to a certain extent by professional boards through their inaction). We also argue that the law's language often masks that involvement; and explain how states further that duplicity behind legal formalisms. In considering the important role healthcare providers, namely nurses and physicians, play in administering death to the condemned, we assert that nurses and physicians are part of the states' penal machinery in America. Nurses and physicians (as carriers of scientific knowledge, and also as agents of care) are intrinsic to the American killing enterprise. Healthcare professionals who take part in execution protocols are state functionaries who approach the condemned body as angels of death: they constitute an extension of the state which exercises its sovereign power over captive prisoners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.035 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".