Goal-Based Standards, Meta-regulation and Tripartism in Arctic Shipping: What Prospects in Canadian Waters?
Bibliographic record
Abstract
This chapter looks at alternative forms of regulation of Arctic shipping. Traditional prescriptive regulatory concepts have many problems. These include, inter alia, a lack of flexibility in a technologically vibrant environment, a rigid and minimalist response by regulatees and a reactive approach by regulators, economic inefficiency, imbalanced expertise, barriers to open markets, problems with transparency and accountability, and regulator capture. An alternative approach would be meta-regulation, which is essentially modified self-regulation monitored by regulators. Another concept, utilized by the International Maritime Organization (IMO), is goal-based standards, wherein regulators set basic end-goals and allow regulatees to develop rules that are designed to reach those goals. While most Canadian rules governing Arctic shipping are still prescriptive in nature, the adoption of numerous IMO conventions gives some prospect for the development of a more goal-based or flexible regulatory system. Input and consideration of the interests of impacted Indigenous communities is another key concern. The concept of tripartism, in which “third party” interests are included in the regulatory development process, has the advantage of adding a further knowledge base and of making the interests of impacted communities part of a more flexible and inclusive regulatory system.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".