Revisiting Family Leisure Research and Critical Reflections on the Future of Family-Centered Scholarship
Bibliographic record
Abstract
In this special issue of Leisure Sciences, we examine the progress made and challenges ahead in research on leisure and families—20 years revisited. We consider what advancements have been made in family leisure research and potential new directions that family-centered scholars can look towards. We also consider the dominance of particular theoretical perspectives and methodological designs, and the limitations and consequences of such perspectives, to understand the complexities, diversity, and richness of the lived family experience. Emphasis is placed on the need for scholarship that explores diverse constructions of family and to provide a call to action for family-centered scholars to engage with broader global social issues.
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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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.050 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".