ENVIRONMENTAL IMPACT ASSESSMENTS AND IMPACT BENEFITS AGREEMENTS: THE PARTICIPATION OF ABORIGINAL WOMEN AT VOISEY’S BAY MINE
Bibliographic record
Abstract
Resource extraction projects in the North are governed by negotiated agreements developed between industry, the state and Aboriginal governments and institutions. This thesis examines the role played by women in the Environmental Impact Assessment (EIA) and Impact Benefit Agreement (IBA) processes leading up to Voisey’s Bay mine in northern Labrador and whether women’s involvement in resource governance improves the participation and retention of women in non-traditional jobs at the mine. Using a qualitative methodology of semi-structured interviews and thematic analysis, this thesis found that the participation of Aboriginal women was unable to significantly improve the work experiences of women at the mine. The concerns of Aboriginal women were identified by analyzing submissions made to the EIA panel by women’s groups. These concerns were then compared with the perceptions of work by women who worked in either construction or the operations phase of the mine. The confidentiality of IBA negotiations and documents are offered as one reason that Aboriginal women did not have the concerns they raised during the EIA process mitigated. The unfinished IBA was referred to by VBNC, and accepted by the panel, as a way to mitigate women’s concerns despite confidentiality preventing the contents of the IBA from ever being known. While women received prioritization in the IBA, Aboriginal women demanded quotas and targets for the training and hiring of women for the construction and operations phase. The thesis ends with a discussion of ways to alleviate the conflict between IBA and EIA 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".