The Justiciability of Climate Change: AComparison of\nUS and Canadian Approaches
Bibliographic record
Abstract
Climate change-related disputes, which often include novel, complex,or politically sensitive matters, have experienced a mixed reception by the courts. Defendants both in Canada and the United States have raised the issue of justiciabilitythe question of whether a matter is of the quality or state of being appropriate or suitable for review by a court-with some success in attempts to have these cases summarily dismissed. The author reviews the types ofclimate change cases that have been launched, examines the US and Canadian laws of justiciability analyzes the.paths in which the caselaw regarding justiciability in these countries is headed, and suggests how these developments will impact future climate change cases. This paper finds that Canadian courts may be increasingly using justiciability as a means to avoid addressing climate change issues-just as US courts may be beginning to take a more progressive approach.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.030 | 0.032 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".