An initial design validation framework for cooperative risk management in seaports
Bibliographic record
Abstract
Recent disruptions in supply chains, such as the COVID-19 pandemic, have highlighted the importance of cross-organisational risk management for avoiding or mitigating the impacts of operational risks such as supply bottlenecks and demand shocks. Earlier work has presented the CoRiMaS risk management model for seaports, in which stakeholder analysis, risk governance, strategic and tactical risk management, and knowledge management are the key aspects. Considering the lack of validation approaches for cooperative risk management in the general risk literature, and a fortiori in the maritime domain, this article proposes an initial validation framework to test the design of the CoRiMaS risk management model for seaports. Apart from introducing the conceptual basis and practical steps of this validation framework, it is applied to an illustrative case study to clarify its concepts, and to guide further testing and research. The presented case study includes specific scenarios that were discussed with stakeholders in Germany and Finland. The developed framework can be used and expanded to validate the design of similar cooperative risk management models. The results of the illustrative test case based on the initial design validation framework support the development of a theoretically founded cooperative risk management auditing process.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".