Bibliographic record
Abstract
Abstract Investment treaties grant special international protection to foreign investors, and give them a means to enforce those rights against States in which they have invested. This book examines systematically the law of international investment treaties. Although the precise provisions of investment treaties are not uniform, virtually all investment treaties address the same issues. This book examines those issues in detail, including the scope of application, conditions for the entry of foreign investment, and general standards of treatment of foreign investments. Investment treaty law has continued to evolve rapidly and dramatically since publication of the second edition of this work in 2015. The field has seen considerable growth in the number and scope of investment treaties, now estimated at 3300, and investor-state arbitrations cases, which reached over 1000 in 2020. Beyond growth, the field has also experienced significant changes and reforms. In 2018, eleven Pacific Basin Countries, despite the withdrawal of the United States, forged ahead to conclude the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTTP), a potentially far reaching regional trade and investment agreement. The next year, the three north American nations replaced the North American Free Trade Agreement (NAFTA) with the United States-Mexico-Canada Agreement (USMCA). And in 2020, European Union member states terminated over 100 intra-EU BITs, leaving intra-EU investors to rely on EU law and legal processes alone for protection from unfavourable government acts. This edition incorporates a consideration of all of these and other reforms into its analysis of the body of law created by investment treaties since World War II.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 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".