The Principle of Equal Treatment in International Arbitration
Bibliographic record
Abstract
This chapter offers a contribution to the discourse on procedural equality in international arbitration by explaining its interaction with other basic norms that govern the arbitral process and proposing a framework to assess equality claims.The principle of equal treatment or procedural equality is a core adjudicative ideal that has a long history dating as far back as the Magna Carta Libertatum. Together with the right to an impartial and independent tribunal and the right to be heard, the principle of equal treatment provides a foundation for the arbitral process that is essential to ensure its legitimacy. The principle of equal treatment pervades every aspect of the arbitral process and must be given due regard at each stage of the proceedings: at the stage of tribunal constitution, when joining additional parties, allocating time, determining the scope of privilege or allowing non-disputing third parties to intervene, among others. While the importance of procedural equality in international arbitration is today well-established, less attention has been paid to how claims of equality ought to be assessed. Drawing on jurisprudence on equal treatment protections in international human rights law, this chapter proposes a two-step inquiry that first considers whether there is a rational basis for any differentiated treatment between the parties, before analysing whether the differentiated treatment creates an unfair disadvantage.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".