What You Don't Know Can Hurt You: Investor-State Disputes and the Protection of the Environment in Developing Countries
Bibliographic record
Abstract
Recent years have seen a substantial increase in investor-state disputes. In many cases matters of public interest, including environmental regulations, are being tried. While it is crucial to assess the outcomes of investor-state disputes that involve matters of public policy, the procedures followed in investment arbitration make this difficult and, in some cases, impossible. This is relevant not only for researchers, but also crucially for regulators. This article focuses on how the lack of transparency in arbitration, and the lack of consistency of tribunal decisions, creates uncertainty for regulators. This uncertainty, when combined with the financial risk involved in proceeding to arbitration, may create situations in which the threat of an investment dispute is sufficient to convince a government to reverse, amend or fail to enforce an environmental regulation-a phenomenon referred to as regulatory chill. These issues are explored in an Indonesian case involving a dispute over mining contracts in protected forests.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".