Turning to international litigation to protect the Amazon?
Bibliographic record
Abstract
Abstract Safeguarding the Amazon biome remains a critical priority, not only for the eight Amazon River basin States but also for the world at large given the ecosystem’s planetary importance. There is now a new sense of urgency surrounding Amazon protection given the substantial increase in rates of deforestation and fires in the region. Accordingly, new options to advance Amazon protection are beginning to be explored, including proceedings in international courts and tribunals. This article provides a critical assessment of the potential for such litigation, examining the possible claims that could be advanced, the risks associated with each of these and which are more likely to be successful. It argues that the litigation landscape is complex, and there are jurisdictional, normative and evidentiary hurdles in the way of a clear‐cut judgment requiring Amazon States to take the urgent and direct measures needed to bring the ecosystem back from the brink.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 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".