Bibliographic record
Abstract
Drawing on nurses' strikes in many countries, this paper explores nurse militancy with reference to professionalism and the commitment to service; patriarchal practices and gendered subordination; and proletarianization and the confrontation with healthcare restructuring. These deeply entangled trajectories have had a significant impact on the work, consciousness and militancy of nurses and have shaped occupation-specific forms of resistance. They have produced a pattern of overlapping solidarities--occupational solidarity, gendered alliances and coalitions around healthcare restructuring--which have supported, indeed promoted, militancy among nurses, despite the multiple forces arrayed against them. The professional commitments of nurses to the provision of care have confronted healthcare restructuring, nursing shortages, intensification of work, precarious employment and gendered hierarchies with a militant discourse around the public interest, and a reconstitution and reclamation of 'caring', what I call the politicisation of caring. In fact, nurses' dedication to caring work in the late twentieth and early twenty-first centuries may encourage rather than dissuade them from going on strike. This paper uses a trans-disciplinary methodology, qualitative material in the form of strike narratives constructed from newspaper archives, and references to the popular and scholarly literature on nursing militancy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.009 |
| 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".