(In/Ex)clusive fitness cultures: an institutional ethnography of group exercise for older adults
Bibliographic record
Abstract
Abstract Older adults benefit greatly from being physically active yet they are the least active generation. To appeal to older consumers, to reduce barriers older adults experience to becoming physically active and to increase the number of physically active older adults, the exercise market has been divided into mainstream fitness and age-segregated programming that specifically targets older adults. This research employed an institutional ethnography approach to understand better the social discourses and material practices that shape socially (in/ex)clusive physical cultures for older exercisers in both mainstream and older-adult group exercise classes. Textual analyses, interviews and field observations revealed that the material and discursive work practices intended to promote inclusivity in group exercise physical cultures actually engendered age-exclusive markets. Herein, we discuss how the guidelines and policies put forth by these certifying bodies, and the training curricula they publish, govern group exercise practices in a manner that tends to align with dominant ideological discourses conflating age and ability. We conclude by arguing that in order to create more inclusive physical cultures, mainstream fitness providers need to embrace options that appeal to potential group exercise consumers of all abilities, regardless of age.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".