Innovative training strengthens recreation capacity across Canada’s North
Bibliographic record
Abstract
Communities across Canada’s North are small and isolated. Geography, climate, small populations, high transportation costs, and a shortage of qualified staff are barriers to delivering recreation (Sparks, 2011). Yet, meaningful and culturally-relevant recreation programs and services are essential to physical and mental health and social well-being. Recreation delivery across the North is hampered by a shortage of skilled staff. Qualified staff are hard to recruit while local residents cannot access industry certification, on-the-job training, nor the post-secondary education needed to establish careers in recreation. The Recreation North Training Program was developed to address these challenges. Evaluation (Frank, 2018; Riessner, 2020) has found the Recreation North Training Program to be a valuable alternative for Northerners. Core competencies for working in the recreation field are developed through interconnected, micro-learning events. Training is delivered through weekly conference calls, online content and discussions, and the application of learning to work settings. Remote delivery and the use of tools appropriate to the availability of technology and bandwidth; a strong learner-centred approach; and a focus on local community and culture contribute to a training experience that is inclusive, accessible, and responsive to the diversity of participants from across Canada’s North.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".