Physical leisure for any-body: imagining inclusive possibilities through body mapping
Bibliographic record
Abstract
For decades, the fitness industry has been critiqued for valorizing narrow white-able-lean centric body ideals – against which all bodies are evaluated or deemed nonnormative. Our Canadian-based study resists these standards by centring nonnormative embodiments. Given the centrality of the body in our research, we used body mapping, an arts-based method, to elicit participant stories that visually reflected their experiences and meanings of physical leisure, defined broadly as physical activities done during leisure time. Six participants who self-described as queer, fat, disabled, trans, and/or neurodivergent each completed online body mapping workshops and created body maps. Narrative themes presented in our findings are titled, “Yes, and … ” (embracing the coexistence of opposing truths); “radical defiance” (offering alternative difference-affirming ways to reclaim bodies and movement); and “reclaiming joy” (participants’ demand for more joy in exercise). Through body mapping, participants visually articulated their experiences, rooting self-trust in the gut and their embodied realities. We also explore the possibilities and limits of body mapping in understanding how nonnormative bodies can imagine inclusion in physical leisure, emphasizing the need for intentional, accessible approaches. Our study highlights the potential of visual methods in reimagining and re-mapping bodies, particularly those marked by social and other demarcations of difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".