Climate Change in Tort Law? The New Regulatory and External Effect of Suits against Private Actors
Bibliographic record
Abstract
Tort-based climate-change suits against private companies have begun to surface in Europe. This chapter identifies three different strategies for the use of tort law in such suits and shows how the regulatory effect has become still clearer. It goes beyond the traditional deterrent effect of tort law and obliges not only compensation for damage suffered in the past but also policy changes for the future. In addition, this regulatory effect may well reach beyond the borders of the EU. Firstly, basic tort law concepts may be exportable to other jurisdictions with similar tort law traditions. Secondly, since private companies form part of large global supply chains, company policies can affect not only the company itself but the entire group of companies as well as contractual partners based outside of the EU. In this way, the regulatory effect of tort law also becomes external. One might speak of a climate change in tort law itself.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".