Gendered Environmental Assessments in the Canadian North: Marginalization of Indigenous Women and Traditional Economies
Bibliographic record
Abstract
This article compares three environmental assessment (EA) cases in Nunatsiavut (Labrador), Nunavut, and the Northwest Territories to better understand how resource decision-making processes in northern Indigenous mixed economies are gendered. Advances in Indigenous jurisprudence and Indigenous peoples’ assertions of their rights to lands and territories have influenced new cooperative resource management institutions and associated environmental assessment frameworks. Though previous research has pointed to the systemic ways in which EAs undermine self-determination, there has been little attention to how gender influences EA processes and outcomes. This article contributes to emerging scholarship on gender and EAs through a thematic analysis of the environmental assessments for the Voisey’s Bay Mine and Mill in Nunatsiavut (1997); the Meadowbank Mine in Nunavut (2004–2006); and the Mackenzie Gas Project (2003–2009). The cases examined reflect a spectrum in the extent to which gender is accounted for and attended to in EA processes. Indigenous women’s interventions in each case challenged the narrowly scoped treatment of gender in EA processes by describing their broad concerns with development. In each case, EA processes emphasized participation in employment rather than community well-being, and inadequately addressed women’s traditional harvesting activities. We argue that in failing to account for the totality of northern livelihoods, the EA process privileges resource extraction, re-inscribes gender hierarchies, and undermines Indigenous mixed economies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.044 | 0.020 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".