Bibliographic record
Abstract
In order to effectively educate youth in regards to avalanche safety there must be more than one approach. Realizing that youth are keen and curious but generally lack internal motivation, are conscious they are going to be scrutinized by their peers, unconsciously feel they are indestructible, can be strongly influenced by the lack of adult safety culture around them and are confined financially; youth avalanche education can be challenging. Other identified barriers to youth avalanche education include program funding, different user groups, a wide spectrum of education of the educator and a limited willingness to go beyond avalanche awareness into avalanche education. By providing a multiple layered approach youth avalanche education can go beyond these barriers and be successful. Through trial and error, the Canadian Avalanche Centre (CAC) has explored different approaches such as educating the educator, running specific youth avalanche courses, youth avalanche awareness presentations, providing materials/equipment for educators and youth groups, and using social media as an education tool. As well, the CAC has worked collaboratively with a number of other organizations and institutions providing youth avalanche education. By analyzing the strengths and weaknesses of each approach the CAC hopes to share the knowledge gained, help other organizations promote youth avalanche education and open discussion on youth avalanche education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".