Bibliographic record
Abstract
The precautionary principle is a legal principle that has found considerable support in international environmental law. Its emergence, however, has not been without problems and controversies: how do we define its normative content and trigger elements, and how do we ensure concrete implementation. The 2011 Seabed Mining Advisory Opinion used states’ due diligence obligations to prevent harm to realize the precautionary principle. Focusing on this case, this article examines how the precautionary principle can be applied using the concept of due diligence. First, this article explores the precautionary concept using examples from a selection of regional and multilateral environmental instruments, analyzing its origin and different expressions and identifying the problems in its application. Second, the article analyzes the Pulp Mills case and the Seabed Mining Advisory Opinion to substantiate the role of the obligation to take precautionary measures in the legal framework of due diligence. Third, by reference to the International Law Commission’s Draft articles on Prevention of Transboundary Harm from Hazardous Activities and the International Law Association’s study report on the Legal Principles relating to Climate Change, along with a number of international cases, the article further illustrates the distinction between due diligence, prevention and precaution and argues that they are actually interrelated.
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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 | 0.047 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".