Arctic Corridors and Northern Voices: governing marine transportation in the Canadian Arctic (Cambridge Bay, Nunavut community report)
Bibliographic record
Abstract
Ship traffic in the Canadian Arctic nearly tripled between 1990 and 2015. Most of that increase happened in Nunavut waters. Between 1990 and 2015 Cambridge Bay had the third highest increase in vessel traffic in Nunavut. This increase can be explained by the increasing number of vessels transiting the Northwest Passage, including passenger ships, pleasure craft, tankers, and general cargo ships. The Government of Canada is developing a network of low-impact marine transportation corridors in the Arctic that encourages marine transportation traffic to use routes that pose less risk and minimize the impact on communities and the environment. The Low Impact Shipping Corridors will be a framework to guide future federal investments to support marine navigation safety in the North, including improved charting and increased hydrography, in partnership with Northerners. The corridors initiative is co-led by Transport Canada, the Canadian Coast Guard, and Canadian Hydrographic Service. Key considerations in the current prioritization of the corridors include identification of Inuit and northerners’ perspectives on 1) the potential impact of marine vessels on marine areas used for cultural and livelihood activities, and on community members; and 2) potential management strategies for the corridors. This report reflects opinions gathered through participatory mapping and focus group discussions with Cambridge Bay community members who were identified by the Ekaluktutiak Hunters and Trappers Organization as key knowledge holders. Analysis was aimed at understanding Inuit and northerners’ perspectives on the potential impact of marine transportation on local marine use areas and community members, and on identification of potential management strategies for the low impact shipping corridors. This report was validated by the research participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".