Flight Experience, Risk Taking, and Hazardous Attitudes in Glider Instructors (Experience de vol, Prise de Risque et Attitudes Dangereuses des Instructeurs de vol sur Planeur)
Bibliographic record
Abstract
Abstract : Crew members' hazardous attitudes (including invulnerability to stressors) have been identified as possible contributing factors to many aviation accidents, and a great deal of research in this area thus far has been directed toward developing effective training programs to modify them to reflect realistic and positive attitudes towards flight safety. Yet little research has explored the role of flight experience and risk−taking attitudes in explaining hazardous attitudes, especially outside the context of general aviation. The current work extends existing research by examining the hazardous attitudes of glider instructors. It also investigates the role played by flight experience and risk−taking attitudes in predicting the instructors? hazardous attitudes, considering both the linear and curvilinear relationships between flight experience and hazardous attitudes. These cross−sectional data, originating from 144 current and past glider instructors from five Regional Gliding Centres across Canada, provided partial support for the hypotheses. Of note were the significant quadratic components of the overall main effect of flight experience on hazardous attitudes. As well, greater risk−taking attitudes were significantly related to greater negative attitudes toward human factors, as was a basic knowledge of human factors. I summarize the findings and present limitations of the study as well as suggestions for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".