Leisure education and indigenous knowledge systems in South Africa: a commentary
Bibliographic record
Abstract
The role of leisure education in equipping individuals and society to participate in leisure is well-researched in scholarship. In this commentary, we argue that incorporating indigenous knowledge systems (derived from South Africa’s (SA) diverse peoples, cultures and languages) in leisure education may promote inclusivity, cultural relevance and alternative leisure perspectives. Drawing from various scholarly perspectives, this article defines leisure education, revisits the concept of leisure, explores the concept of indigenous knowledge systems and presents a case for the inclusion of indigenous knowledge in the SA context. The implications that are drawn from the discussion call for a re-thinking of leisure education so that it responds to SA’s contextual realities through the strategic incorporation of indigenous knowledge. This may contribute towards contextualized leisure research methods, wholesome leisure education content, creative resource-attainment strategies, novel knowledge-transfer methods and unique skill-equipping techniques that may advance leisure wellness, social cohesion and the leisure profession in SA.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".