(Trans)forming fitness: Intersectionality as a framework for resistance and collective action
Bibliographic record
Abstract
Fitness is a lifelong pursuit, yet many LGBTQ2S+ 1 individuals are averse to group fitness or experiences in big box gyms. Due to the COVID-19 pandemic, virtual fitness programs offered the potential to facilitate opportunities for the greater inclusion of such individuals and the chance to connect, collaborate and advocate for a change in who and what defines fitness. Justice Roe, owner of Fit4AllBodies, utilizes the term fitness industrial complex to provide a framework to discuss the problems of exclusion. His explanation supports research documenting that bodies that are not “the norm”, defined by ableism, classism, (hetero)patriarchy and racism, fueled by white supremacy, are oftentimes viewed as “less than” in the fitness and recreation world ( 1 – 3 ). Applying an intersectional framework, this article explores the possibilities for transformative collective action in fitness communities that removes barriers and challenges the injustices that contribute to racialized LGBTQ2S+ individuals feeling unwelcome. With the need to shift to virtual training spaces as a result of a global pandemic, and the rise in the public discourse surrounding racial injustices both on and offline, a sense of belonging and community is important, especially among groups that often face exclusionary practices, such as racialized LGBTQ2S+ community members. These individuals are at greater risk of losing opportunities to access fitness programs that can provide immense health and psychological benefits. What could an intersectional perspective on resistance in sport look like? Using the example of LGBTQ2S+ access to online fitness spaces during the prolonged global COVID-19 pandemic starting in 2020, we suggest that explicit coaching education and intentional communities, centered around social justice, are needed to address the historical roots of systemic oppression, accessibility, and social constructs tied to fitness.
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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 | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.018 | 0.115 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".