What is Leisure? The Perceptions of Recreation Practitioners and Others
Bibliographic record
Abstract
The purposes of this research were to determine if agreement exists among leisure services practitioners regarding the meaning of leisure and to examine how they describe themselves and the body of knowledge related to leisure services. In addition, these responses were compared with a group of individuals outside the field to determine if these practitioners possess a unique understanding of leisure, leisure practitioners, and the body of knowledge. Members of the Recreation Branch of the Ohio Parks and Recreation Association (n = 108) and a purposive sample of employees of two local adoption agencies (n = 30) completed questionnaires, including a True/False section, a three-part free-list component, and demographic information. Data were analyzed according to consensus modeling theory using Anthropac™ data analysis software and SPSS™. The True/False data indicated high agreement, and thus, “culturally correct” definitions of leisure for each group that support traditional and multidimensional definitions of leisure. When analyzed along with the free-list data, the most frequently reported dimensions of leisure paralleled traditional definitions (i.e., free time, activities). The responses of both groups indicate that professionals need to know about management and activities. Implications of these findings are discussed in relation to models of service provision.
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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 | 0.007 | 0.016 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".