A comparative analysis study of overseas advanced cases on ski instructor training programs and evaluations: Focusing on Canada
Bibliographic record
Abstract
The purpose This study compared the ski instructor education system of Canada, known as an advanced country in overseas skiing, to strengthen the capabilities and cultivate leadership of domestic ski instructors. Accordingly, the purpose of the study is to derive implications for the curriculum, content, and evaluation of domestic ski instructors. This study analyzed data using a literature analysis method. In order to select cases for study in this study, Canada"s ski instructor system was set as an advanced example based on countries that have consistently maintained winning records in various world competitions. Canada"s ski instructor curriculum, content, and evaluation were set as a unit of analysis to collect data and conduct content analysis. As a result of the study, there is no pre-certification education program in the domestic ski instructor training program, and the qualification system is implemented through written experiments and practical ability tests. Accordingly, it is believed that if new field-centered ski instructor education programs and certification contents are developed and managed through the Canadian ski instructor qualification system, the ski participation population will increase and the educational capabilities of ski instructors will be improved.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".