CrossFit and “Cancel Culture”: Probing Practitioners’ Responses to the “Canceling” of Greg Glassman
Bibliographic record
Abstract
In this article, we explore the responses of crossfit practitioners to the 'canceling' of Greg Glassman in the aftermath of racist tweets and comments made in response to the killing of George Floyd. We draw on 50 interviews with crossfit practitioners to understand how they interpret and respond to the 'canceling' of Greg Glassman and the disavowal of CrossFit by prominent CrossFit athletes and organizations. We probe how athletes, regardless of levels of involvement, in the wake of Glassman's comments respond to the refiguring of the sporting community of CrossFit. A cancel culture continuum from affirmation to rejection emerged from the interview data that typified their views of cancel culture, Greg Glassman's removal from CrossFit HQ, and the current state of the sport. We conclude with a discussion of the phenomena of canceling or cancel culture and reflects on crossfit as a sport in light of the Glassman affair.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Interview study of CrossFit practitioners on cancel culture; the OpenAlex topic label 'Academic Freedom' is misleading, the object is a sport community.
This studies CrossFit practitioners' responses to cancel culture, not research.
Sociology of sport study of CrossFit practitioners’ responses to cancel culture.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".