Rethinking the ‘Fit’ in Fitness: Misfitting (Loudly) to Transform Physical Activity Futures
Bibliographic record
Abstract
The fitness industry has long glorified an “ideal body.” Anyone not “fitting” this ideal is cast as non-normative, and likely excluded, discriminated against, and pressured to conform to mythical standards. Using the feminist materialist concept of misfitting in our narrative thematic analysis, we thought through 22 participants’ stories of in/exclusion in the world of physical activity and fitness. Participants recognized being a misfit (noun) in juxtaposition to the white-able masculine standard. They also discussed how the norm is enforced, including stories of punitive exercise, fear of judgment, and performative worth(iness). Choreographing misfitting shares participants’ responses to being a misfit, via choreographing invisibility (by avoidance or seeking to mask/pass) and choreographing visibility (to expose unrealistic standards of normativity by misfitting (verb) loudly). We argue that misfits must always be centered and celebrated for teaching us how physical activity and fitness can and should be inclusive for all.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.062 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".