An initial evaluation framework for the design and operational use of maritime STAMP-based safety management systems
Bibliographic record
Abstract
Abstract A safety management system (SMS) is the common means used by organizations to assess organizational performance with respect to the safety and well-being of people, property and the natural ecosystem. A SMS provides confidence to diverse stakeholders that organizational safety is at an appropriate level and fulfils the applicable regulatory standards. As a multifaceted system for organizational safety assessment, ensurance and assurance, the evaluation of the design and operational use of SMS is a complex process. An evaluation needs to provide evidence about how well the design and operation of an SMS complies with applicable standards and how well the methods used in the SMS implementation support the organizational policies and practical work. In the maritime domain, SMS is broadly applied. However, there are few theoretically rooted SMS design approaches, and there is a lack of frameworks to evaluate how well the SMS is designed and how effectively it operates. This paper proposes an initial evaluation framework for the design and operational use of a maritime SMS design approach based on Systems-Theoretic Accident Model and Processes (STAMP), realist evaluation and Bayesian Networks. This framework is applied for a case study of vessel traffic services (VTS) Finland to test its relevance and ability to guide the SMS design. The experiences gained in the case study, and the related discussion on the framework, can guide further research in this area. Ultimately, the work can be used as a basis for developing maritime SMS auditing processes, based on specific theoretical and methodological approaches.
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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 | 0.050 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".