Leisure, deviant leisure, and crime: “Caution: Objects may be closer than they appear”
Bibliographic record
Abstract
The purpose of this paper is to highlight the imprecise definitions and likelihood of significant potential overlapping relationships among the concepts of leisure including casual and serious leisure (Stebbins, 1997; 1999); deviant leisure; and crime. Indeed, categorizations have been made between casual and serious leisure, normal and deviant leisure, while criminal behaviour may be included as a subset of deviant leisure (Rojek, 1999a). Although at face value the relationship between crime and deviant leisure appears to be somewhat forthright, what is much less apparent is the possibility that in specific cases, common and important variants of normal leisure may also overlap with criminal motivations and behaviours. Similarly, boundaries between normal and deviant leisure also may be blurred. Using a multidisciplinary approach that incorporates both leisure and forensics sciences, we suggest a new positioning of these various constructs in relation to each other, which may substantially impact the ways “leisure,” “deviant leisure,” and “crime” are conceptualized and operationalized by leisure and criminology scholars and professionals. We also propose a typology based on the work of Stebbins (1996, 1997) for better understanding the different dimensions of deviant leisure as it may relate to current views of leisure and crime.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".