HOW SAFE IS SAFE? A CANADIAN AIR CARRIERS (CAC) SAFETY BEHAVIOR INVESTIGATION
Bibliographic record
Abstract
The importance of safety within an organization is determined by the implementation of a Safety Management System (SMS), organizational culture, management commitment and behaviour, the activity of staff themselves, and to what degree safety reporting is upheld (Cohen, Wiegmann and Shappell, 2015). Canada was the first country globally to implement regulation mandating a Safety Management System (SMS) program. Many Canadian air carriers (CAC) proudly announce safety as a top priority, which is achieved through their SMS program. Amidst aviation’s verbal safety saturation, safety is often communicated as the top priority within the industry; however, are the public declarations consistent with CAC practices? This paper investigates whether safety behaviour within CAC is aligned to the objectives of the SMS. In-depth interviews with seven senior safety experts were conducted to identify areas of improvement and a survey with 164 respondents. This research found that there are many areas of improvement of the safety performance of CAC. Factors, which affect safety reporting behaviour and the priority of safety, include management’s support of a safety culture, job function, and the number of air carriers an individual has worked for. This research also suggests that a job function that was created to instil public confidence is more likely to deviate from safety procedures and less likely to report. A template for safety success, which influences organizational culture resulting in economic viability output, is proposed and recommendations for safety culture enforcement by the regulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".