Leisure Education, Poverty and Recreation Participation
Bibliographic record
Abstract
Abstract Individuals living in poverty face a number of challenges that interfere with their ability to create or engage in meaningful leisure and recreation experiences. Although limited access to finances is often the most cited constraint to leisure and recreation participation among those living in poverty, there are a wide range of other constraints on participation: limited leisure skills; knowledge and interests; discriminatory policies; feelings of guilt and shame; and poor physical, social and mental wellbeing. Despite the vast array of policies, subsidized programming and supports that the recreation profession and allied professionals have made available, low-income families continue to struggle to access and create leisure experiences. This case study highlights the innovation of a community-based leisure education delivery system to help low-income families learn about leisure, develop the necessary skills, knowledge, capacities and resources, and gain access to a wide range of leisure and recreation experiences to enhance their leisure repertoire. The success of the agency is embedded in their invested stakeholders, their ability to utilize existing community leisure recreation resources, and their focus on teaching for and through leisure. Because of this agency’s unique approach, the application of leisure education at the community level has positively impacted not only the child, but the family unit and community as well. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © C. Hiebert and J. Oncescu 2015
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".