The central role of Inuit Qaujimaningit in Nunavut’s impact assessment process
Bibliographic record
Abstract
In impact assessment (IA) the value of different forms of knowledge is increasingly acknowledged, but implementation and practice challenges continue. In Nunavut, a territory in the Canadian Arctic, Indigenous knowledge plays a key role in understanding and defining environmental baselines and guiding the assessment process; however, even here there are needs and opportunities for improved treatment and use of Indigenous knowledge in assessment and decision-making. This paper outlines the central role of Inuit Qaujimaningit/Qaujimajatuqangit (IQ) (Inuit knowledge) in shaping and defining Nunavut’s impact assessment process. The work highlights the potential of the Nunavut process to provide a model for the use of Indigenous knowledge in IA, and of co-management or Indigenous-led impact assessment. Focus groups were held with board members and staff of the Nunavut Impact Review Board – the co-management board responsible for impact assessment in the territory. The results highlight the unique qualities of the impact assessment process in Nunavut and demonstrate how IQ is a crucial component of project review, notably its role in decision-making and for ensuring that the process is meaningful to communities. The results and recommendations have value to a range of other jurisdictions that are also working towards using Indigenous knowledge in environmental decision-making or even seeking to advance Indigenous-led impact assessment.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".