Modernizing the Governance of Passenger Vessel Operations in the Canadian Arctic
Bibliographic record
Abstract
The uncoordinated governance of passenger vessel operations in the Canadian Arctic has produced an unnecessarily complex permitting system. This chapter utilizes process mapping, an international jurisdictional scan, and a multidisciplinary literature review to re-evaluate Canada’s Arctic passenger vessel governance and permit requirements. It expands on previous research findings through the inclusion of macro-level constraints, including the complexity and dynamism of the external environment, the rapid pace of technological change within the international shipping sector, and the impetus for a re-delegation of tasks between organizations within the existing governance arrangement. The findings suggest that the permitting system cannot be effectively streamlined via a temporary horizontal coordination mechanism, as more systemic reforms are required to coordinate Canada’s Arctic passenger vessel governance. While critics may argue that the current volume of polar passenger vessel traffic does not warrant the costs of creating a new alternative service delivery agency, this chapter recommends that an ‘Arctic Passenger Vessel Coordination Entity’ be established under Transport Canada’s portfolio to foster the horizontal integration of services between departments, the vertical integration of services across governments, and a more efficient passenger vessel permit system in the Canadian Arctic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".