Death-Making: Nursing Neutrality, Biopower, and Institutional Complicity
Bibliographic record
Abstract
This paper critiques the enforced neutrality of the American nursing profession, and positions it’s neutrality as a mechanism of institutional power that sustains structural violence. Drawing on theoretical concepts of comfort, parrhesia, and biopower, the paper examines how professional norms, framed as “objectivity” and “civility”, discipline nurses into silence in the face of fascism, racism, state violence, and global injustice. Institutional responses to dissent are analyzed as affective and biopolitical strategies that prioritize comfort in protection of dominant power structures, and render parrhesia, or political truth speaking, deviant. In light of the suppression of abolitionist and anti-colonial discourse in professional spaces, the paper argues that silence is not passive, it is death-making. This paper calls for a reimagining of nursing as a site of collective care, resistance, and ethical refusal aligned with movements for mutual aid, healing justice, and abolitionist praxis. In doing so, it insists that nursing’s future lies not in neutrality, but in the healing practice of discomfort and resistance.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.095 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".