NIRB’s Inchoate Incorporation of Inuit Qaujimajatuqangit in Recommendation-Making Under Nunavut’s Impacts Assessment Regime
Bibliographic record
Abstract
In 1999, the comprehensive claims of the Inuit of Nunavut were settled against Canada which culminated in the rati cation of the Nunavut Land Claims Agreement (NLCA). The NLCA was given legal effect through the federally enacted Nunavut Land Claims Agreement Act and Nunavut derived its existence as a territory in the federation from the federally enacted Nunavut Act. The Nunavut Planning and Project Assessment Act (NUPPAA), is a federally enacted statute which came into force in 2014, adds to the impact assessment regime provided for under Articles 11 and 12 of the NLCA. The Nunavut Impact Review Board (NIRB) requires project proponents to not only incorporate traditional knowledge—more specifically, Inuit Qaujimajatuqangit (IQ)—into the baseline collection and methodologies of resource management in their project proposals, but to further outline where management strategies, mitigation and monitoring plans, and/or operational considerations employ IQ values and knowledge. Our analysis reveals that there is inchoate incorporation of IQ into NIRB processes by the NIRB itself and argues that the NIRB ought to better incorporate IQ into its decision and report-making processes.
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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 | 0.053 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.006 |
| 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".