Mapping Human Rights-Based Climate Litigation in Canada
Bibliographic record
Abstract
In line with global trends, there has been an increase in human rights-based climate litigation brought in Canadian courts in recent years. A federal state comprised of ten provinces, three territories, and diverse Indigenous peoples (First Nations, Inuit, or Métis), Canada provides a rich and multifaceted case study. In our article, published in a special issue of the Journal of Human Rights and the Environment, we consider three different dimensions of Canadian climate litigation: substantive human rights arguments; procedural environmental human rights claims; and the potential of transnational corporate accountability human rights-based claims.\nIt is no surprise that the experience of climate change across the Canada is not uniform, given its geographic scope and diversity. For example, the Inuit have been long aware that climate change poses a serious threat to human rights as evident from the petition to the Inter-American Commission on Human Rights in 2005. More recently, the wildfires and extreme heatwave in Western Canada and concern over flooding and sea-level rise among coastal communities has drawn attention to the urgency of adaptation. However, the economy remains heavily tied to the fossil fuel industry particularly in Alberta and in Newfoundland & Labrador, and Canadians have among the highest per capita CO2 emissions in the world. Canada has repeatedly failed to meet its own inadequate climate mitigation targets, yet a 2007 legal challenge to this failure was held to be non-justiciable. More recent legal challenges to federal carbon pricing legislation came from provinces who viewed it as federal overreach, however the majority of the Supreme Court of Canada held in favour for the federal government in 2021, noting in passing that climate change is a serious threat to Indigenous peoples, including their ability to maintain traditional ways of life.\nAgainst this background, our article considers emerging trends in human rights-based claims.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.030 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".