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Record W2566848084 · doi:10.1080/11745398.2016.1250646

The normalization of sport for older people?

2016· article· en· W2566848084 on OpenAlexafffund
Michael Gard, Rylee A. Dionigi, Sean Horton, Joseph Baker, Patricia L. Weir, Claudio Dionigi

Bibliographic record

VenueAnnals of Leisure Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsYork UniversityUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNormalization (sociology)PsychologySociologyAdvertisingGerontologyMedicineBusinessSocial science

Abstract

fetched live from OpenAlex

Opportunities for older adults in Western countries, particularly women, to participate in physically demanding, competitive sports have increased since the 1960s. Now, coinciding with the neoliberal shift in social policy, older adults live at a time when physical activity is highly encouraged through ‘healthy or active ageing’ discourses in media, policy policies and the sport/exercise sciences. This study sought to understand how 63 Masters athletes (aged 60 and over) explain their participation in sport and, in particular, the extent to which they use neoliberal language of personal moral responsibility and economic efficiency to explain their own participation and the non-participation of older adults in sport. While degrees of moral talk were evident in the older athlete responses, in almost all cases, non-participation in sport was seen as irrational and in need of explanation. Overall, our findings suggest that older people’s participation in sport has been, or at least is in the process of being, normalized among participants in Masters sport. We discuss how this changing idea about sport and ageing might reshape social policy, as well as social relationships, between older people and the state and between different groups of older people.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 head0.224
GPT teacher head0.526
Teacher spread0.302 · 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

Citations66
Published2016
Admission routes2
Has abstractyes

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