Does Indigenous Knowledge Occurin and Influence Impact Assessment Reports? Exploring Consultation Remarks in Three Cases of Mining Projects in Greenland
Bibliographic record
Abstract
Mineral extraction is pursued in Greenland to strengthen the national economy. In order that new industries promote sustainable development, environmental impact assessments and social impact assessments are legally required and undertaken by companies prior to license approval to inform decision-making. Knowledge systems in Arctic indigenous communities have evolved through adaptive processes over generations, and indigenous knowledge (IK) is considered a great source of information on local environments and related ecosystem services. In Greenland the Inuit are in the majority, and Greenlanders are still considered indigenous. The Inuit Circumpolar Council stresses that utilizing IK is highly relevant in the Greenland context. Impact assessment processes involve stakeholder engagement and public participation, and hence offer arenas for potential knowledge sharing and thereby the utilization of IK. Based on the assumption that IK is a valuable knowledge resource, which can supplement and improve impact assessments in Greenland thus supporting sustainable development, this paper presents an investigation of how IK is utilized in the last stages of an impact assessment process when the final report is subject to a hearing in three recent mining projects in Greenland.
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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 | 0.070 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".