The role of project-based impact assessment in considering the impacts of resource development related Arctic shipping
Bibliographic record
Abstract
Transportation by sea is the main method for the movement of goods in the Arctic. With longer ice-free periods, and new technology, the increases in ship traffic experienced over the past few decades are expected to continue. Resource development projects are an important source of ongoing increases in regional shipping. My thesis attempts to understand the potential of Nunavut’s impact assessment (IA) framework to meaningfully identify and address the impacts associated with project related shipping. To achieve this purpose, I conducted a literature review and document review of several recent IAs in Nunavut. To enhance the data collected through the document review, I carried out interviews with experts and participants of the IAs studied. The results of my work indicate that IA in Nunavut routinely includes shipping impacts within the scope of assessment, and many shipping related concerns have been documented throughout IA proceedings. Further, my findings indicate that IA can influence project shipping through mitigation measures and consultation requirements. However, my data also reveal that important factors serve to limit the reach of project-IA when attempting to impose conditions on project shipping that exceed the requirements of regional shipping regulations. One example of this relates to the lack of spill response capacity and the implications of this for the Canadian Arctic. Nonetheless, my findings demonstrate that IA is an important forum for resource management in Nunavut, and that IA offers critical opportunities for shipping impacts to be addressed on a project basis moving forward.
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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 | 0.069 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".