An Exploratory Application of the International Risk Governance Council’s Risk Governance Framework to Shipping Risks in the Canadian Arctic
Bibliographic record
Abstract
The diminishing extent of sea ice in Arctic areas brings opportunities for increased shipping activities in the Canadian Arctic. However, it also causes concerns, e.g., related to environmental pollution to vulnerable areas and impacts on ecosystems at local, regional, and global scales, which can further impact human health. Increased shipping activity also causes concerns about safety risks associated with the navigation of vessels, for instance, related to the response to vessels or people in distress. Appropriate risk management strategies, tools, and equipment are essential to successfully mitigate these risks, with due consideration of concerns of rights-holders, stakeholders, and society at large. In this chapter, an exploratory application of key elements of the International Risk Governance Council (IRGC) risk governance framework is presented, focusing on selected risks associated with shipping in the Canadian Arctic. After introducing the IRGC framework, selected shipping risks in the Canadian Arctic are classified in terms of the type of risk problem these represent. Subsequently, a discussion is given on the implications of this pre-screening for selecting appropriate risk governance strategies. The chapter concludes with a discussion on suggestions for future work on risk governance in a Canadian Arctic maritime shipping risk context.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".