“It would be a lot easier to hunt whales if they didn’t move.” Addressing marine baseline information challenges in Nunavut’s impact assessment process
Bibliographic record
Abstract
Abstract Despite advances in impact assessment (IA) practice in Arctic regions, persistent challenges remain. This article examines how baseline information needs and associated uncertainties are presented and understood in the regulatory context of IA. The focus is on marine-related information needs in the Nunavut IA process. The method used a document review of operational IA reports and focus groups with the Nunavut Impact Review Board – the agency responsible for IA in the territory. The results show that information challenges are largely linked to the availability, suitability and accessibility of data; while challenges to addressing information needs are related to broad capacity constraints, as well as responsibility, and cooperation among parties to the process. Similar to other settings, in Nunavut, there is a need to develop better guidance for parties regarding information uncertainties in IA and how such may be addressed. To help address information needs, there is also a need to clarify the roles, responsibilities and expectations of all parties (e.g. Inuit organisations, proponent, government and communities), as well as improving coordination and advancing collaboration, while also addressing capacity constraints.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".