Bibliographic record
Abstract
The diversity of activities in a repertoire of leisure is a variable shown to have considerable impact on the quality of leisure and beneficial personal outcomes, and it is proposed as one important indicator of cultural capital. Theoretical perspectives on cultural capital indicate the importance of education in preparing individuals for broad patterns of leisure consumption in addition to status attainment. Contemporary advances demonstrate that status attainment cannot be equated with high cultural consumption and that broadly omnivorous leisure pursuits may be more valuable to social actors. Role attachment theory and disengagement theory are additionally explored as possible theoretical explanations that assist in predicting leisure diversity that is highly patterned by employment and one's age. The number of different leisure experiences that constitute an individual's leisure repertoire is expected to change throughout the life-course as needs for cultural capital vary and as demands in paid and domestic work change. Tobit models of leisure diversity are proposed using American Time Use Survey (ATUS) data. Findings indicate that leisure diversity is impacted by ethnicity, recent immigration, age and socio-economic status, and theoretically relevant conditional relationships are explored.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".