Bibliographic record
Abstract
Leisure as a tool for rehabilitating people with neuro-disabilities is well established. Yet, despite significant progress in this area, problems remain in the way leisure is used for this purpose. One, as yet, unresolved problem is how to determine which leisure activity or activities will be attractive to people with particular disabilities. Another is how to counteract the persistent, dominant public view that real personal worth is measured according to the work people do rather than the leisure they pursue. The third is to inform practitioners, many of whom are unaware of recent advances in leisure theory, about these advances, which can help them solve the first problem and adapt to the second. The main body of this paper presents such a theory - the serious leisure perspective. It synthesizes three main forms of leisure, showing, at once, their distinctive features, similarities, and interrelationships. The forms are serious, casual, and project-based leisure. A review of the research on neuro-rehabilitation follows. Some implications of the Perspective for neuro-rehabilitation are then presented, including ways practitioners can introduce clients to certain types of leisure, encourage them to pursue the types chosen, and help them develop an optimal leisure lifestyle.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".