Enabling conditions and challenges to environmental assessment as a tool for knowledge brokerage: lessons from Nunavut
Bibliographic record
Abstract
Knowledge brokering is a process of communication and interaction aimed at knowledge exchange and learning between parties with different knowledge bases This paper examines knowledge brokering in Arctic environmental assessment (EA) as a mechanism to support learning, build capacity, and sharing power. The study assessed enabling conditions and challenges to knowledge brokering in the eastern Canadian Arctic under the Nunavut EA process, managed by the Nunavut Impact Review Board. Methods included focus groups and review of legislation, process documents, and EA guidance to examine how the regulatory environment and Board’s EA process support knowledge brokering. Results illustrate that if EA is to support knowledge brokering, it should focus not only on communities but also on EA institutions – those that directly enable and support knowledge brokering, and those that provide ancillary information and have important regulatory roles. Enabling EA as a platform for brokering knowledge also requires investment beyond that provided through project-by-project funding models. While many jurisdictions are catching up to EA systems in Canada’s Arctic, and learning how to incorporate best-practices for Indigenous engagement and knowledge sharing, Nunavut’s co-managed process offers a model and learning opportunities for exploring the implementation conditions necessary for effective knowledge brokering through EA.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".