“As a resistor, you are not alone”: Locating the collective in uncoordinated acts of professional resistance
Bibliographic record
Abstract
INTRODUCTION: When trainees encounter social harm and injustice in clinical and educational settings, they engage in acts of professional resistance. These efforts can either be coordinated or uncoordinated and implemented as individuals or collectives. Although it is easy to see the relationship between the collective and individuals in coordinated acts, it is unclear what role a collective plays in uncoordinated resistance efforts. This study investigated the role of a larger collective, including whether such a collective exists, among a group of trainees engaged in professional resistance. Specifically, we were interested in what trainees contribute to and draw from these collectives as they address social harm and injustice within medical education. METHODS: Trainees were recruited through professional networks and snowball sampling, with in-depth interviews conducted in two phases. Phase one included interviews with 18 trainees from the U.S. and Canada, and phase two involved re-interviewing 13 of them. We used constant comparative analysis and a social movements framework (collective identity, framing processes, resource mobilization and strategies) to analyse the data. RESULTS: Despite a lack of coordination, trainees consistently narrated a reliance on a larger collective, which they actively curated to include individuals from outside of medicine. Trainees drew from this collective a shared identity and a unifying 'injustice frame' for understanding social harm. In return, they contributed new strategies and tactics, which they shared with their colleagues. However, trainees did not receive emotional support or resource mobilization from these collectives. A few trainees expressed a desire for more coordinated action, whereas one reported feeling alienated by it. DISCUSSION: Our findings demonstrate that uncoordinated resistance is not an isolated endeavour but is sustained by a dynamic, reciprocal relationship with a broader, self-curated collective. Although this collective provides a shared identity and a steady influx of new strategies, it may not be able to offer the emotional support and resource mobilization necessary for more sustained, coordinated change.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.035 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".