Assessing Gross Disproportionality in Climate Change Litigation: The Case for a Justificatory Approach
Bibliographic record
Abstract
Canadian climate change litigation is testing the scope of section 7 of the Charter . The young claimants in La Rose v Canada and Mathur v Ontario argue that governments’ insufficient efforts to reduce greenhouse gas emissions violate their right to life, liberty, and security of the person. This article addresses a central challenge in such litigation. Neither international law nor the Charter provides clear criteria for quantifying the obligations of individual states to reduce greenhouse gas emissions. Under these circumstances, how can courts assess whether Canada’s or Ontario’s policies are insufficient and infringe section 7 interests in a grossly disproportionate manner? The article proposes that courts should not attempt to define absolute mitigation obligations. Rather, the inquiry must be a relative one. Courts should begin by determining the global warming trajectory with which Canada’s and Ontario’s emissions align and then ask whether the harm to section 7 interests expected at that level of warming is justified in light of the cost of achieving further emissions reductions. Because such an evaluation depends on risk assessment and political priority-setting, courts should afford governments a broad measure of deference while demanding that they clearly articulate and justify their choices.
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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 | 0.176 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.026 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".