Climate-Proofing Judicial Review after Paris: Judicial Competency, Capacity, and Courage
Bibliographic record
Abstract
We attempt to unpack the concerns at the core of categorical judicial deference toward government and administrative agency environmental assessments (EAs) and project decisions in Canada. These concerns vary by decision-maker and statutory context, and while sometimes made explicit, they are often left unarticulated and unexamined. We demonstrate that that while categorically deferential judicial review of EAs is a significant obstacle to Canada meeting its climate change mitigation and sustainability commitments, particularly when based on especially broad statutory language, Canadian courts are nonetheless capable of overcoming them. Ultimately, we argue that robust judicial review of EAs having climate change and sustainability implications can play an important role both in helping Canada move toward its climate and sustainability targets, and ultimately in diminishing the frequency with which EAs are litigated on judicial review. Indeed, we argue that a robust judicial review regime is a critical precondition of timely and efficient EA processes, and that such timeliness and efficiency will become increasingly important in the context of charting legal pathways to deep decarbonization and scalable renewable energy generation pursuant to the Paris Agreement and the UN’s Sustainable Development Goals.
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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 | 0.062 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".