Diversity in the Investor-State Arbitration: Intersectionality Must Be a Part of the Conversation
Bibliographic record
Abstract
This article examines the contemporary discourse on diversity in the field of investment arbitration, and finds that conceptually the aspect of 'intersectionality' is overlooked. The parties, arbitration institutions, law firms and arbitrators themselves pledge to increase diversity in the field by appointing more female arbitrators without asking which women to appoint. Female lawyers come from various backgrounds, for example, there are female lawyers from developing countries, black female lawyers, indigenous female lawyers, Asian female lawyers, etc. Their backgrounds do not constitute a single dimensional characteristic, instead, they can overlap, creating unique obstacles for 'entry' into the field as an arbitrator, a position of legal authority and prestige. The article seeks to contribute to the discourse on diversity by examining the concept of intersectionality, and its relevance to the ongoing attempts of the participants of the investment regime to diversify the pool of candidates for the arbitral bench. The article examines the list of ICSID cases, and the list of the investment cases in which Canada was a respondent to conclude that the vast majority of female candidates that are appointed to the investment panels continue to be Caucasian women from developed states. The article provides an overview of options for diversifying the pool of arbitrators, and points the direction forward for the diversity discourse.
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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 | 0.034 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.070 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".