Bibliographic record
Abstract
Abstract The legal imagination framing ISDS practice is closely related to how arbitrators connect storytelling with the interpretation of investment treaties and other relevant laws. This chapter examines several influential awards, placing the facts of each dispute within the broader social and political context of the investment, the conflict, and the economic sector. The analysis highlights how arbitrators think of foreign investment relations, and the extent to which they focus on, or silence, issues of distribution, recognition, and embeddedness. The cases are discussed chronologically in order to identify what has changed—and what has not—in arbitrators’ reasoning. Cases covered in this chapter include Santa Elena v. Costa Rica, Metalclad v. Mexico, TecMed v. Mexico, SD Myers v. Canada, Methanex v. USA, Glamis v. USA, Chemtura v. Canada, Occidental v. Ecuador 2, Philip Morris v. Uruguay, and Eli Lilly v. Canada.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.006 |
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".