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Record W3153560470 · doi:10.1017/s0144686x21000507

(In/Ex)clusive fitness cultures: an institutional ethnography of group exercise for older adults

2021· article· en· W3153560470 on OpenAlexaff
Kelsey Harvey, Meridith Griffin

Bibliographic record

VenueAgeing and Society · 2021
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMainstreamAppealEthnographyIdeologyGerontologyPhysical fitnessCurriculumSociologyPsychologyGender studiesSocial psychologyPublic relationsPolitical sciencePedagogyMedicinePoliticsPhysical therapyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.307
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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