Why Due Regard Is More Appropriate than Proportionality Testing in International Investment Law
Bibliographic record
Abstract
Abstract Global regulatory standards of due diligence, regulatory coherence, and due regard are emerging in public international law. Investment law has been concerned to settle upon the most appropriate regulatory coherence tests for application in the arbitration of regulatory disputes. Candidates have included proportionality, rationality, and reasonableness tests. This article argues instead for reliance on the due regard standard in conjunction with reasonableness or rationality testing. This will more appropriately reflect the nature of investment treaties as inter-State bargains. Further, responding to arguments for the adoption of proportionality on the basis of comparative public law, the article demonstrates that proportionality is not established as a general head of review in common law jurisdictions including England, Australia, Canada, New Zealand and South Africa. At the same time, the application of the due regard standard can have much in common with procedural proportionality testing as seen among these domestic legal systems and elsewhere.
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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 | 0.108 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.018 |
| 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".