Opportunities and strategies for effective management of low impact Arctic shipping corridors
Bibliographic record
Abstract
Ship traffic has been increasing across the Canadian Arctic over the past decade and additional growth is expected as climate change continues to enhance navigability in the region. In response, the Government of Canada (GOC), including the Canadian Coast Guard, Transport Canada, and Canadian Hydrographic Service are developing a set of ‘Low Impact Shipping Corridors’ to support shipping governance. The objectives of the corridors are to; 1) establish incentivized and voluntary corridors; 2) provide marine navigation safety support; and 3) respect local cultures, ecology, and the environment. The GOC is currently engaging rights holders and stakeholders in an official capacity to promote discussions on the location and desired governance of low impact shipping corridors. The study presented here is separate from this official GOC activity and was designed as an independent research project that may aid GOC and other decision makers in the development and implementation of effective corridors governance. The specific purpose of this study was to identify and evaluate potential governance strategies that can aid in the effective management of Canada’s growing Arctic marine vessel traffic through a Low Impact Shipping Corridors approach and to enhance understanding of the opportunities and challenges related to governing marine vessel traffic across Inuit Nunangat and Arctic Canada. The research team undertook an iterative three-part survey (Policy Delphi) in which expert rights holders and stakeholders contributed their knowledge and perspectives on 1) strengths and weaknesses of the corridors framework; 2) potential management strategies that could aid in the effective governance of Canada’s growing Arctic marine vessel traffic through a corridors approach; and 3) what type of governance body may best suit regional and local needs. Participants identified a range of strengths and weaknesses of the corridors initiative including, for example, the need for enhanced marine navigationand safety, minimization of ecological and cultural impacts, guiding effective infrastructure and service investments, and shared leadership and collaborative management. From the suite of strengths and weaknesses, participants identified a total of 45 corridors-management strategies that could potentially enhance related strengths and mitigate weaknesses that were revealed. The suite of potential management strategies was organized into relevant thematic areas, which included 1) Governance and Regulation, 2) Resources and Services, 3) Knowledge Mobilization and Communication, 4) Culture and Environment, and 5) Research and Monitoring. Each individually identified strategy was carefully evaluated using a structured rubric by a 31-member expert panel based on five factors including a) affordability, b) implementability, c) effectiveness, d) co-benefits, and e) timeframe for implementation. Affordability and implementability collectively can be considered measures of overall ‘feasibility’. Levels of consensus among the expert panel members was considered, and where a significant divergence of opinion emerged it was taken into consideration when ranking priority management options (i.e., only strategies receiving high and/or medium levels of consensus among the expert panel members were listed as priority management strategies). Results of the analysis revealed a total of ten management strategies ranking the highest including: establishing a 'one stop shop' public website for corridors-related information; establishing a single point of contact that Inuit Nunangat community members can connect with if they observe non-compliance of regulations; publicly sharing the names of vessels that violate corridors regulations; providing real-time digital maps of the corridors to all operators; modernizing navigation aids; investing in modern charting; establishing a network of digital communications infrastructure; creating educational material and providing fuel spill kit training for Inuit Nunangat communities; and sharing of key features in the corridors such as Culturally Significant Marine Areas (CSMA) and Ecologically and Biologically Significant Areas (EBSA) with ship operators for consideration when navigating in the area. When considering principles for effective corridors management, two overarching ideas emerged from the expert panel, including the idea that corridors management should be responsive, and inclusive, as well as dynamic.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".