Gendering Environmental Assessment: Women’s Participation and Employment Outcomes at Voisey’s Bay
Bibliographic record
Abstract
This paper examines the effect of Inuit and Innu women’s participation in environmental assessment (EA) processes on EA recommendations, impact benefit agreement (IBA) negotiations, and women’s employment experiences at Voisey’s Bay Mine, Labrador. The literature on Indigenous participation in EAs has been critiqued for being overly process oriented and for neglecting to examine how power influences EA decision making. In this regard, two issues have emerged as critical to participation in EAs: how EA processes are influenced by other institutions that may help or hinder participation and whether EAs enable marginalized groups within Indigenous communities to influence development outcomes. To address these issues we examine the case of the Voisey’s Bay Nickel Mine in Labrador, in which Indigenous women’s groups made several collective submissions pertaining to employment throughout the EA process. We compare the submissions that Inuit and Innu women’s groups made to the EA panel in the late 1990s to the final EA recommendations and then compare these recommendations to employment-related provisions in the IBA. Finally we compare IBA provisions to workers’ perceptions of gender relations at the mine in 2010. Semi-structured interviews revealed that, notwithstanding the recommendations by women’s groups concerning employment throughout the EA process, women working at the site experienced gendered employment barriers similar to those experienced by women in mining elsewhere. We suggest that the ineffective translation of EA submissions into EA regulations and the IBA, coupled with persistent masculinity within the mining industry, weakened the effect of women’s requests for a comprehensive program to hire and train Indigenous women.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".