Comparative study on overseas advanced ski instructor qualification systems : Focusing on New Zealand, Germany, Japan, and Canada
Bibliographic record
Abstract
This study seeks to derive implications for establishing the qualification system and strengthening the capabilities of domestic ski instructors. Accordingly, the purpose of the study is to analyze the domestic ski instructor qualification operation system and the ski instructor qualification system cases of advanced countries such as New Zealand, Germany, Japan, and Canada, and to improve the ski instructor qualification operation system and program to suit the domestic situation and explore alternatives. This study selected four countries (New Zealand, Germany, Japan, and Canada) using the case study method. Considering the set case analysis target and unit of analysis, research papers, reports, press releases, offline data related to the ski instructor qualification system, and internet data from the websites of related organizations were collected and used for comparative analysis. Research results: First, in the case of advanced skiing countries overseas, the effectiveness of qualification education is sought by categorizing and grading ski instructors by level and target, and then constructing scientific theories and practical education programs that meet each educational purpose. Second, the higher the level of the ski instructor training course in advanced countries overseas, the more tests are conducted for more than three days. Third, even after obtaining ski instructor qualifications, various competitions and classes are held for ski instructors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".