From Lapland to Labrador : exploring the potential of indigenous participation in environmental impact assessments
Bibliographic record
Abstract
Environmental impact assessment (EIA) is a common planning tool used to assess the impacts of proposed development projects and will play an important role in sensitive Arctic ecosystems as the demand for development increases.There is criticism of how indigenous peoples are involved in EIA despite international political and academic recognition of its value.To analyse this discrepancy two frameworks were developed.The ILO Convention No. 169, The Indigenous and Tribal Peoples Convention, was reviewed for the first framework and eight components related to the rights of indigenous peoples regarding natural resource management were identified to represent an international standard for involvement.Academic literature was analysed for the second framework and 22 components of successful collaborative environmental management (CEM) were identified.The language in the four pieces of EIA legislation affecting the Innu in Labrador, Canada and the Sámi in Lapland, Finland was analysed to determine how well the components of the two frameworks were met.For the CEM framework, only 16 components were able to be assessed by looking at legislation and the other six were excluded from this study.The results of this content analysis found that the Canadian Environmental Assessment Act, 2012 met both frameworks the best, followed by Newfoundland and Labrador's Environmental Protection Act, the EU's EIA Directive, and lastly Finland's Act on the Environmental Impact Assessment Procedure.Analysis of the results in the context of existing literature on the implementation of EIA legislation and indigenous peoples' involvement identified areas where the jurisdictions can learn from one another and where the legislation can be improved.This research supported 20 recommendations to help inform policy decisions and better align current EIA processes with the international and academic standards of indigenous peoples' participation in environmental management.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".