Sustainable International Investment Agreements: Challenges and Solutions for Developing Countries
Bibliographic record
Abstract
In this book chapter, Mayeda reviews how developing countries can use international investment agreements (IIAs) to promote their sustainable development policies. He first identifies some of the deficiencies of existing models, and then suggests new provisions that developing countries should consider including in future IIAs in order to provide them more policy flexibility to deal with political, social and economic emergencies. Suggestions include integrating principles of sustainable development into the preamble and objectives of these agreements, as well as modifying typical substantive provisions such as MFN, national treatment, the prohibition on expropriation and fair and equitable treatment in order to enable developing countries to protect the environment and promote human rights. The author also explores how environmental and social impact assessments can be integrated into the investment approval process. Finally, the author addresses effective enforcement mechanisms, including liability for investors in their home state, international liability under the treaty, and liability in the host state.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".