Protecting the Health and Safety of Pilots: A Critical Analysis of Flight and Duty Time Regulations in Canada
Bibliographic record
Abstract
Civil aviation is the most regulated and likely the most competitive mode of transportation in the world. When commercial air transportation gained its economic momentum in the middle of the twentieth century, \nstrong competition forced emerging airlines to find new ways to increase their profitability, such as optimising the utilisation of their pilots to reduce labour costs. \nAs new technology allowed pilots to fly transcontinental flights for extended hours, government regulators in the 1960s quickly realised that pilot fatigue was a developing threat to the travelling public. By focusing \non human factors in the context of flight safety, flight and duty time (FDT) regulations were adopted to limit the number of hours airline pilots spent flying and working on duty. \nThis article will analyse the current FDT regulations in Canada. While many Members States of the International Civil Aviation Organization (ICAO) have modernised their FDT regulations in the last few years, Canada’s regulatory approach to mitigate pilot fatigue is clearly outdated. This article will critically evaluate the existing and potential shortcomings of the pilot fatigue regulations currently in force in Canada. There is a \ngenuine feeling in the industry that these regulations and current laws are inadequate or obsolete, as they do not reflect modern pilot fatigue science and place smaller carriers flying in unorthodox environments at risk.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".