Legal Issues Related to Developing Safety Management Systems and Safety Risk Management at U.S. Airports
Bibliographic record
Abstract
Safety Management System (SMS) has been defined as a systematic approach to managing safety not only by proactively conducting safety assessments before there is an incident or accident, but also by having the necessary policies, procedures, organization structure, and accountabilities in place. SMS has four key elements: 1) Safety Policy, which defines the methods and tools for achieving safety goals, including management accountability for such goals; 2) Safety Risk Management (SRM), which requires a proactive approach to identifying risks, quantitatively and qualitatively categorizing risks, and establishing mitigation for identified risks; 3) Safety Assurance, which includes a method for establishing processes to monitor an organization’s performance in identifying risks and establishing preventive or corrective actions to maintain safety; and 4) Safety Promotion, which involves the establishment of procedures and processes that change the safety culture and environment, including the establishment of confidential reporting systems, to encourage employee reporting and feedback as well as employee training. The identification of risks and the creation of such records could increase airports’ liability as entities subject to their individual state sunshine laws. The result could be less data obtained as confidentiality of data is crucial to those reporting information. Since SMS has been in effect at airports around the world, experiences from Europe, Canada, and Australia are discussed. SMS has been implemented in other industries in the United States, including the maritime, oil, and gas industries, as well as in the area of patient safety. These are examined in the context of U.S. airport implementation. While this digest does not evaluate the FAA’s Notice of Proposed Rulemaking, it does provide an evaluation of SMS and the issues that airport operators must consider and address when establishing SMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".