Understanding the Relationship between Race and Leisure Activities and Constraints: Exploring an Alternative Framework
Bibliographic record
Abstract
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].
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".