Canada’s Pro-Ban Stance on Double-Hatting: Playing the Long Game in ISDS Reform?
Bibliographic record
Abstract
Abstract The practice of arbitrators and counsel in investor-state dispute settlement (ISDS) cases simultaneously playing both roles — known as “double-hatting” — has been the subject of much controversy in recent debates on ISDS reform, notably, at the United Nations Commission on International Trade Law’s (UNCITRAL) Working Group III where a Draft Code of Conduct for Adjudicators in International Investment Disputes is under discussion. While Canada has been less than consistent in its approaches to ISDS in recent international investment agreements (IIAs), its position against double-hatting has been rather constant. This article explores whether this stance reveals a commitment on the part of Canada towards increased judicialization of ISDS or reflects a “flavour of the month” reform likely to change with differing IIAs and negotiating partners. Analysis of Canada’s recent IIA practices, including its model Foreign Investment Promotion and Protection Agreement, released in May 2021, and the positions it has taken at UNCITRAL’s Working Group III, lead the author to conclude that Canada appears committed to increased judicialization of ISDS in the long run.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".