Bibliographic record
Abstract
Climate advocates are increasingly raising specific climate change concerns before domestic courts, human rights tribunals, international commissions and other national and international decisionmaking bodies. Win or lose, these litigation strategies are significantly changing and enhancing the public dialogue around climate change. This article discusses the awareness-building impacts of climate litigation as well as related impacts such strategies may have on the development of climate law and policy. The article argues that litigation's focus on specific victims facing immediate threats from climate change has increased the political will to address climate change both internationally and nationally. It has also shifted the debate towards questions of compensation and adaptation, and has brought new and democratic voices to the climate policy debate. As a result, climate litigation is leaving an important imprint on climate policy regardless of whether a tort action in the United States or the Inuit human rights claims, for example, ultimately prevail - and as demonstrated by the recent US Supreme Court decision in Massachusetts v. EPA, some climate claims will prevail, setting important precedents for the future direction of climate law and policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".