Quiet quitting: Obedience <i>a minima</i> as a form of nursing resistance
Bibliographic record
Abstract
In this article, we provide a philosophical and ethical reflection about quiet quitting as a tool of political resistance for nurses. Quiet quitting is a trend that gained traction on TikTok in July 2022 and emerged as a method of resistance among employees facing increasing demands from their workplaces at the detriment of their personal lives. It is characterised by employees refraining from exceeding the basic requirements outlined in their job descriptions. To understand why quiet quitting can be a tool of resistance useful for nurses, we first draw on Frédéric Gros' concept of 'surplus obedience' and Michael Lipsky's notion of 'routines and simplification strategies' to highlight the ethical implications associated with nurses engaging in and sustaining harmful systems, such as the neoliberal healthcare system. Leaning again on Gros, we then propose that 'obedience a minima', a concept akin to quiet quitting, can serve as a method of ethical nursing resistance. After describing what the concept entails, we provide a discussion emphasising the potential of obedience a minima as a one method, among many, that can be leveraged by nurses to challenge and resist a system that prioritises financial considerations over patient wellbeing. The article concludes by reflecting on the ethical nature of resistance in the context of nursing, that is the act of obeying oneself and refraining from participating in systems that are detrimental to the lives of Others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".