Bibliographic record
Abstract
The concept of resistance in nursing has been garnering more interest in the last few years, with emerging focus on working conditions, power differentials in clinical settings, health inequities, and planetary health concerns. As a result, it's important to identify what is being resisted, and what is the purpose of the resistance carried out. In whatever way resistance is referenced in nursing, outright or not, it is our contention that it's in response to the same underlying cause, barring some local and contextual variations, which we refer to as 'the Beast', where the real catastrophe is societal, and is 'existential, affective and metaphysical'. It therefore seems coherent to consider this macro catastrophe from an ontological point of view, that is, from the standpoints of 'being' in relation to the world, which necessarily refers to specific ways of apprehending reality. In this article, we therefore present two ontologies - antagonistic in every respect, to better situate resistance in nursing in a larger ecosystem. Using the Invisible Committee's book and call to action To our friends, this is our modest contribution to celebrate resistance, to help equip fellow nurses to better organise and strategize in the face of incessant growth and too often undesirable change in healthcare.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.077 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".