Agents of care and agents of the state: bio‐power and nursing practice
Bibliographic record
Abstract
AIM: This paper presents a conceptual analysis of the concept of bio-power in the context of nursing, including a critique of the widespread rhetoric that nursing is deprived of power and consequently is an apolitical agency. BACKGROUND: Traditionally, power tends to be defined in terms of repression, interdiction and punishment. On the contrary, work by Michel Foucault with regard to bio-power brings into evidence the productive and positive nature of power at the heart of society. Despite being often used by various academic and professional disciplines, the concept of bio-power is rarely cited in nursing. FINDINGS: Nursing as a profession is at the heart of bio-power in that nurses lie at the crossroads between the anatomo-political and bio-political ranges of power over life. They therefore contribute to social regulation through a vast array of diverse political technologies. Nurses are at the flexing point of the state's requirements and of individual and collective aspirations. They occupy a strategic position that allows them to act as instruments of governmentality. Consequently, nurses constitute a fully-fledged political entity making use of disciplinary technologies and responding to state ideologies. CONCLUSION: The concept of bio-power offers a rich theoretical perspective for nursing, as it questions the definition of nursing care as neutral and mainly provided according to patients' best interests.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".