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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".