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Record W3132039475 · doi:10.1177/1558866121995165

How Constraints to Campus Recreation Participation Differ Based on Activity Type, Gender, and Citizenship

2021· article· en· W3132039475 on OpenAlexafffundabout
Vinu Selvaratnam, Ryan Snelgrove, Laura Wood, Luke R. Potwarka

Bibliographic record

VenueRecreational Sports Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsRecreationFeelingCitizenshipLogistic regressionPsychologySocial psychologyCenter (category theory)SociologyApplied psychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the differential effects of constraints on participation in three different types of campus recreation (i.e., intramural sports, drop-in sports, fitness center), and how constraints differ based on gender and citizenship. Data were collected from undergraduate students ( n = 344) using a questionnaire at a large university in Ontario, Canada and analyzed using logistic regression and Mann–Whitney U. Non-participation in intramurals was associated with not knowing how to get involved, drop-in sports with not knowing enough people to participate, and fitness center with feeling uncomfortable exercising in public. Women and men did not differ in the ten constraints measured in the study. International students were more constrained than domestic students by feeling as though the recreation facilities were inaccessible. Implications for practice are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.332
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2021
Admission routes3
Has abstractyes

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