‘Where is the space for continuum?’ Gyms and the visceral “stickiness“ of binary gender
Bibliographic record
Abstract
This paper develops a visceral feminist geography of the gym to expand our understanding of how everyday physical activity environments are implicated in the gendered context of physical activity. The gender gap in physical activity is well-documented, with women around the world less likely than men to meet the minimum physical activity recommendations for health. Fitness gyms are popular venues for physical activity, but they are not necessarily inclusive places. Through a reflexive thematic analysis of interview and journaling data with 52 Canadian women and men gym users, we identify five visceral domains through which the gym enacts gender boundaries: the imaginary, bodily haptics, the soundscape, visual fields, and material “stuff“. Each of these revealed a series of gendered dichotomies that, taken together, contribute to an overarching gender binary of unbounded masculinity and bounded femininity. We argue that these “visceralities“ matter because the gym as an institution comes to codify gender differences in ways that perpetuate possibilities for practising physical activity as bifurcated ways of doing gender. One of our key findings is how women’s participation in the gym was underwritten by material expense and bodily preparatory practices that extend far beyond the gym into the geographies of their daily lives. Physical activity interventions that do not account for the multisensorial features of place may miss opportunities to reduce gendered inequities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".