How Are We Doing? In Conversation With Amélie Perron, on Nursing as ‘Disobedient Practice: Care of the Nurse's Self, Parrhesia, and the Dismantling of a Baseless Paradox’
Bibliographic record
Abstract
ABSTRACT This article is a dialogue between authors, revisiting Perron's 2013 Nursing Philosophy paper ‘Nursing as disobedient practice: Care of the nurse's self, parrhesia, and the dismantling of a baseless paradox’. Perron's paper argues for the ethical imperative of nursing as political action—including philosophical explorations of disobedience, care of the self, freedom, agency, and ‘parrhesia’: the act of speaking boldly and fearlessly against dominant views of apolitical niceness in nursing. The conversation roots in the present time‐space of declining rights and rising authoritarianism in the United States, Canada, and elsewhere, with risks of harm to nurses and the diverse people and communities that nurses accompany in practices of care, policy, research, and education. Topics discussed include: the historical contexts that sparked Perron's paper, post‐pandemic lessons and implications for nursing, the relationship of nursing organizations to power, ongoing challenges of empowering nurses to become more politically minded and active, and advancing political thought as a morally necessary form of evidence to inform all nursing domains. The conversation concludes with hopes for a collective imagination in nursing grounded in community connections, creative pedagogy, and a bold embrace of theoretical works that inspire and guide nursing actions for justice and equity.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.033 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.020 | 0.049 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".