From the ground up: a study on general aviation flight safety in British Columbia, Canada
Bibliographic record
Abstract
Over a ten-year period, the flight safety trend in Canada shows a 19% decline in\naccidents (TSBC, 2017). However, there is a stark contrast between those incidents involving\ncommercial airline operators, and those of privately operated, termed “general aviation”, aircraft\n(TSBC, 2017). In 2016, the number of flight accidents relating to GA privately operated aircraft\nwas in excess of 120, roughly six times the incidence of all other flying categories (TSBC,\n2017). My research sought to uncover why the rate of GA accidents was much higher than that\nof other categories, with a particular focus on a hypothesized complacency in GA pilots. A\nsecondary investigation objective predicted that aviation culture negatively contributes to flight\nsafety. In phase-I of the research project, a survey was sent to 224 (26.79% response rate)\nmembers of a large general aviation association via email. Ten survey participants were selected\nat random to partake in phase-II of the study. Phase-II involved an interview session followed by\nevaluation in a flight simulator.\nDespite the fact that emergency scenario training is a major component of present day\npilot education regimes, and that survey responses revealed a high comfort and confidence for\nhandling in-flight emergencies, 90% of flight simulator participants failed to recognize the inflight\nemergency from the aircraft’s instrument panel that was presented to them. While many\ndid recover from that emergency, no participant applied the correct steps in the correct sequence,\nas they would have previously demonstrated during their initial flight training to a standard\nworthy for the earning of pilot license privileges (Transport Canada, 2017b). In a second\nsimulated emergency scenario, only 20% of pilots could pre-designate their intentions for\nresolving the issue and then successfully perform to resolve it. These findings are supported by\nstatistical feedback derived from the survey, as well as themes uncovered during the interview.\nBecause the training for pilot licensure would have covered the maneuvers tested during\nthe experiment, that current rules and regulations dictate pilots to undergo some form of\nrecurrent biannual training, and that the majority of GA pilots seem to falsely perceive their own\nability as higher than actual, my research reveals that current educational and regulatory\nstandards surrounding aviation training in Canada is insufficient for preserving aircraft operating\nproficiency in general aviation, non-professional pilots (Transport Canada, 2016a, 2017b).\nFurther, an aviation culture of ego and fear negatively contributes to flight safety by\ndiscouraging effective communication, and creating a blockade to information sharing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".