Qualitative analysis of the final form exams of the skill awards program from Alpine Canada / Kimberley A. Kubeck
Bibliographic record
Abstract
In 1987, Alpine Canada introduced a skill development program \nfor skiers aged 7 to 15 years of age who were enrolled in one of \nCanada's entry level racing programs. One of the objectives of \nthe program is to raise the skiing skill level of Canadian \nyoungsters. The program includes eight levels of skiing \nproficiency. Progression through each of the levels is based on \nthe performance of a final form exam which is the culmination of \nall the basic skill drills at that level. An investigation was \nconducted in order to develop a theoretical model of a giant \nslalom ski turn as the framework for the subsequent qualitative \nanalysis of the skills in the eight final form exams. \nUsing standardized video procedures, data was collected at \nsix different testing sites. Sixty-two performances were selected \nfor qualitative analysis in order to determine; (a) the \nexistence of critical features, and (b) the description of \ncritical features at each of the eight skill levels. The data was \nsubsequently processed using a variety of descriptive techniques. \nThe data analysis resulted in the identification of 14 \nfeatures which were used to anticipate the manifestation of \ncritical features, five features which acted as links between the \nphases of the turn, and eight critical features which were \nfundamental to the efficiency of the turn. Balance constraints \nappeared to take precedence over aerodynamic considerations for \nthe skiers at all eight skill award levels. Although the mastery \nrequirements of the critical features increased from Level 1 to \nLevel 8, individual critical features were not equally weighted \nby all skiers. Variability between performances was attributed to \nthe different ways in which the non-mastered features were \nmanifested. \nFuture research needs to focus on the development of \ndeterministic models for all alpine skiing disciplines. In \naddition, the importance of the development of observation plans \nin order to guide and standardize both quantitative and \nqualitative skill analyses was highlighted.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".