A SYSTEMATIC REVIEW OF SAFETY MANAGEMENT SYSTEM (SMS) IN AVIATION WITH A FOCUS ON THE SAFETY LEVEL
Bibliographic record
Abstract
Safety is generally characterised as the state of being “safe”, the condition of being protected from harm or other non-desirable consequences. One effective way of achieving it is to implement a safety management system (SMS). SMS should be seen as an aggregate strategic aspect of standard business management, understanding its high priority to safety. This article describes and illustrates SMS in aviation, focusing on the similarities and differences in the system approaches adopted by selected Civil Aviation Authorities (CAAs) with the primary focus on the safety level. The main goal is to provide a structural comparison of the system framework within individual CAAs and its explanation in safety-related documents. This article also dealt with the chosen safety approach (reactive, proactive and predictive) and safety performance indicators (SPIs), forming a quality and effective safety system that maintains an acceptable safety level. Finally, this article is mainly based on datasets publicly available through the International Civil Aviation Organisation, Transport Canada, Civil Aviation Safety Authority Australia, Federal Aviation Administration, UK Civil Aviation Authority, Civil Aviation Administration of China and Civil Aviation Authority of New Zealand websites and documentation related to safety.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".