Mapping Canadian Wheelchair Curling Coaches’ Development: A Landscape Metaphor for a Systems Approach
Bibliographic record
Abstract
This study addresses the preintervention phase of a larger project aimed at enhancing the learning capability of the Canadian wheelchair curling coaches’ landscape. To understand the learning leverage features and learning barriers of this landscape, a mapping exercise was conducted. The authors interviewed 16 people, using a semistructured interview guide. The thematic analysis and a landscape metaphor resulted in a map illustrating the main features of the landscape and where the learning potential might be. The findings of this study suggest that geographical isolation, the high costs associated with coach training, and the low number of athletes are all barriers to coaches’ learning. Therefore, with the information gleaned from this phase, an intervention for these coaches should be designed to prioritize meaningful learning opportunities, incorporate influential people noted by coaches, and leverage opportunities at training camps and competitions to mitigate the barriers identified. The landscape view allows for a systems approach that considers the potential of involving the different levels of the sport system to best serve the learning needs of coaches. Rather than focus on individual coach learning, research is needed to better understand how the landscape approach can build learning capability within sport organizations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".