Introduction to ‘Sustainable Development in World Investment Law’
Bibliographic record
Abstract
This volume builds upon previous research on sustainable development in international trade law and policy, published in Sustainable Development in World Trade Law. The volume’s goal is to analyse the state of international investment law through the lens of sustainable development and to clarify how international investment law can contribute to sustainable development. The various chapters in the volume identify, characterize, and analyse existing rules, innovations, and best practices in international investment agreements, including the investment measures used by other sustainable development treaties and instruments. The volume proceeds in four parts. Part I establishes the foundations. Part II of the volume addresses the procedural and substantive dimension of sustainable development in international investment law. Part III is divided into two sections. The first section examines emerging issues and proposals. The second section examines how investment can be promoted through sustainable development treaties in general, followed by chapters on climate change, and biodiversity treaties, as well as treaties concerning water use and shared river regimes. Part IV provides the conclusions, which draw out the themes from this volume and provide a research agenda for the future in this emerging and important area.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".