Inconsistent, downplayed, and pathologized: How mining's gendered impacts are considered in BC environmental assessment
Bibliographic record
Abstract
To what extent do mining environmental assessments in British Columbia (BC) consider gendered impacts? How are they considered? And how are these considerations shaped during the environmental assessment process? To answer these questions we undertook a systematic review of all completed BC mining environmental assessments between 1995 and 2019 (n = 37). Through a careful reading of documentation archived in the BC Environmental Assessment Office registry, we found that 60% of projects did not consider the gendered impacts of mining development; the remaining 40% of projects inconsistently assessed gendered impacts. While noting an increase in gender considerations in environmental assessments since 1995, also quantified in our results is what has not changed. Even where gender is considered, the assessments often collapse this concern into one of “women's issues,” obscuring intersectional impacts and downplaying violence along racialized and gender diverse lines, including those experienced by Indigenous women, children, two‐spirit, trans, queer and non‐binary people. Environmental assessment is a regulatory tool designed to adjudicate the impacts of mining projects, yet our results lead us to conclude that it is also a tool of environmental injustice, compounding and further sedimenting heteropatriarchal and racialized patterns produced through generations of settler colonial resource extraction in BC.
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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 | 0.079 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".