“This Isn’t For Everyone”: The Origins of Defiance and the Preservation of Stigmatized Practices
Bibliographic record
Abstract
While much research and theory has paid attention to how organizational actors stigmatized for their core practices conform to the beliefs and demands of broader and oppositional audiences, we examine the opposite and ask: How do these actors preserve the controversial elements of their stigmatized practices? To address this question, we engage in an inductive historical study of one stigmatized practice, mixed martial arts (MMA), the social sanctions used to inhibit it, and the resistant responses by long-time organizational actors to those sanctions. Our findings suggest that rather than dissuade highly identified core-stigmatized actors, different types of sanctions primed target identities and emotions among them and energized defiance. These stigma-defiant activities included liberation responses, refusing to comply with opponent’s efforts to control them, and inducing covert bargaining with targeted members of monitoring audiences about sanctioning their controversial elements. As a result, these activities created ongoing controversy among monitors and protected ‘perceptually deviant’ elements of such practices by codifying them within the sanctions themselves. In contrast to prior studies, our work suggests the use of stigma to employ sanctions can prime identities and emotions among highly identified, stigmatized actors, driving efforts to preserve controversial elements.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".