Bringing Justice to Environmental Assessment: An Examination of Kearl Oil Sands JointReview Panel and the Health Concerns of the Community of Fort Chipewyan
Bibliographic record
Abstract
Environmental assessment is a critical tool for helping to reduce the environmental impacts of development projects. However, the process as currently designed and implemented does little to ensure that the environmental harms created are fairly distributed among members of the public. Using the human health concerns raised by the community of Fort Chipewyan in the wake of massive oil sands developments upstream as a case study, this paper argues that the Kearl Oil Sands environmental assessment process did little to promote social justice for this community. While the environmental assessment process created an avenue for the community to share its concerns, the process failed to protect against the possibility that these concerns, once voiced, would simply be discounted or marginalized in the determination of what is a “significant environmental adverse effect (SAEE)”. I argue that the duty to apply the precautionary principle now explicitly embodied within CEAA creates a reverse onus upon project proponents to show that their proposal will not create SAEEs and suggest it should be interpreted to include a duty to demonstrate that the environmental harms deemed acceptable will not be shouldered disproportionately by an under-privileged community. I propose a number of reforms that could help transform environmental assessment into a process that promotes environmental justice. To critics who suggest that project-level assessment is not the place to address environmental justice, I argue that environmental assessment processes must not be a mechanism for perpetuating existing systemic inequalities by condoning an unfair distribution of environmental harm. Bringing the question of the distribution of environmental benefits and burdens to the forefront of assessments will lead to greater justice for all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".