Psychology and Social Psychology and the Study of Leisure
Bibliographic record
Abstract
Various social science traditions have influenced theory and research on leisure. In this chapter, we describe psychological perspectives, and these perspectives are primarily those of social psychology with some influences from personality and developmental psychology. Advocates of the development of a social psychology of leisure have generally championed post-positivist psychological social psychological approaches, but interpretive or constructionist sociological social psychologies have contributed as well. These influences are discussed along with other factors that have shaped the social psychological tradition in leisure studies. The frequent claim that leisure research, particularly North American research, is predominantly psychological is also examined. Though clearly focused on individual-level phenomena, we question whether it is substantially grounded in psychological epistemology, methodology and theory. The future of psychological approaches to the study of leisure is explored, including the cross-cultural and international diversity of efforts to understand leisure from social psychological perspectives. We find little evidence that indigenous social psychologies of leisure have emerged in other cultural contexts. However, there are promising social psychological efforts emerging to explore and explain cross-cultural differences and similarities in leisure behaviour and experience. Finally, the growth of interest in leisure as a psychological variable outside of leisure studies and implications for the future are discussed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".