“Add Women and Stir”: The Potential and Limits of GBA+ in Canadian Impact Assessment Law
Bibliographic record
Abstract
Major projects, such as mines, dams, and pipelines impose disproportionate social and environmental harms on marginalized communities. Environmental impact assessment, a central legal framework for approving these projects, has historically failed to identify and address these impacts, thus perpetuating environmental injustice across the country. Changes to the federal impact assessment legislation in 2019 appear to offer a partial response. The new legislation mandates gender-based analysis plus (GBA+) as part of the assessment for major projects. This article considers the potential for mandatory GBA+ to encode intersectionality in impact assessment and begin to address the systemic discrimination carried out through impact assessment laws. It finds that, while the new requirement will make it more challenging for proponents and decision-makers to ignore the allocation of disproportionate burdens and harms, the current framing and implementation of GBA+ represents an additive or check-box approach to addressing discrimination and thus falls short of its intersectional aim.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.052 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".