Litigating Climate Change – Of Politics and Political Questions: A Comparative Analysis of Justiciability of Climate Change in the United States and Canada
Bibliographic record
Abstract
This article seeks to highlight the differences in the jurisprudence on the justiciability of climate change in Canada and the United States. Underpinning this article are questions about the appropriate role of the judiciary in addressing polycentric policy issues. This article will first outline the policy context in which legal issues of climate change are framed. Second, this article will explore the general doctrines of justiciability in Canada and the United States, and how these interrelate with specific doctrines on the justiciability of climate change. The author argues that, with respect to the justiciability of climate change, the approach of the courts in the United States is more principled than that of the Canadian courts, the Canadian approach being more broadly framed. This is because the United States approach encompasses the classic strand of the political questions doctrine. Conversely, though the courts in Canada deny the existence of an American-style political questions doctrine, they unwittingly follow its prudential strand. This has negative implications for legal reasoning. This means that despite contrary appearances from the United States executive, the courts in the United States provide a stronger framework for the protection of the climate.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".