Leisure, deviant leisure, and crime: “Caution: Objects may be closer than they appear”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it