Bibliographic record
Abstract
Traditionally, the vested interests of states concluding bilateral investment treaties (BITs) fell into two categories: on the side of capital-exporting states, an interest in adopting strong protections for foreign investors; on the side of capital-importing states, an interest in attracting foreign investment but also in attempting to preserve host country sovereignty and authority to promote the public interest. Over the past decade or so, however, the line between capital-exporting and capital-importing state increasingly has blurred, and the calculus for states negotiating BITs has become less certain. The experience of the US as a respondent to claims brought to arbitration by Canadian investors under Chapter 11 of NAFTA significantly affected the development of a new generation of US BITs that better balance the interests of host states against those of foreign investors. Similarly, as emerging market economies become significant exporters of capital, these countries are concluding BITs and resorting to investor-state arbitration, driven at least in part by the needs of their own foreign investors. This article examines this convergence in BIT practice. It suggests that these converging trends, a function of states operating behind what Rawls referred to as a “veil of ignorance,” are having and should continue to have a moderating influence on the content of BITs.
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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".