Bibliographic record
Abstract
Conflict between transnational environmental issues and foreign investment in capital-importing states can be commonly found. Actually, several investor-state dispute arbitration cases like Bilcon v. Canada, S.D. Myers v. Canada, and Metalclad v. Mexico concerned environmental matters. States are worried about their measures for securing the environment might be deemed to go against international investment agreements and foreign investors also are anxious because of excessive regulations. Against this backdrop, stakeholders attempt to strike a balance between securing foreign investment and preserving the environment. This article argues that the investment chapter of the Korea-US FTA tries to solve environment-investment collision in investor-state disputes. Before analyzing the provisions of the investment chapter most relevant to environmental issues, this article points out the most typical types of environmental clauses included in international investment agreements. The investment chapter of the Korea-US FTA has provisions which effectively prevent measures from becoming useless when those measures are legitimate measures relevant to environmental matters. This does not mean that the Korea-US FTA completely solves the conflict between environmental issues and the protection of foreign investment, but still it paves the way for a prudent solution which would hash out this thorny problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".