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Record W2007183037 · doi:10.1080/01490400490432109

Understanding the Relationship between Race and Leisure Activities and Constraints: Exploring an Alternative Framework

2004· article· en· W2007183037 on OpenAlexaff
Kimberly J. Shinew, Myron F. Floyd, Diana C. Parry

Bibliographic record

VenueLeisure Sciences · 2004
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRace (biology)SociologyPsychologyLeisure activitySocial psychologyGender studies

Abstract

fetched live from OpenAlex

This study addresses the continuing relevance of race as a persistent societal issue in the U.S., and tackles criticism (Floyd, 1998 Floyd, M. F. 1998. Getting beyond marginality and ethnicity: The challenge for race and ethnic studies in leisure research. Journal of Leisure Research, 30: 3–22. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Henderson & Ainsworth, 2001 Henderson, K. A. and Ainsworth, B. E. 2001. Researching leisure and physical activity with women of color: Issues and emerging questions. Leisure Sciences, 23: 21–34. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Philipp, 1995 Philipp, S. F. 1995. Race and leisure constraints. Leisure Sciences, 17: 109–120. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) regarding the lack of models to guide research that links race and leisure constraints. Shaw's (1994) Shaw, S. M. 1994. Gender, leisure and constraint: Towards a framework for the analysis of women's leisure. Journal of Leisure Research, 26: 8–22. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] framework for analyzing women's leisure is explored as a potential vehicle for understanding racial variation in leisure constraints. More specifically, leisure constraints and preferences of African-Americans and Caucasians were examined, and some of the findings challenge the results of previous studies by suggesting African-Americans are not as constrained as are Caucasians. Our findings do, however, support previous research that indicates the two racial groups have distinct leisure preferences. Several explanations for the findings are discussed, particularly the concept of resistance as introduced by Shaw (1994) Shaw, S. M. 1994. Gender, leisure and constraint: Towards a framework for the analysis of women's leisure. Journal of Leisure Research, 26: 8–22. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar].

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0050.010
Scholarly communication0.0090.010
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.338
GPT teacher head0.389
Teacher spread0.051 · 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

Citations163
Published2004
Admission routes1
Has abstractyes

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