Due Diligence in International Law: A Useful Renaissance or 'All Things to All People'?
Bibliographic record
Abstract
Abstract Few concepts have become as prominent in recent international law publications as ‘due diligence’. Still, its nature and content remain notably ambiguous. This essay reflects on the main takeaways from recent scholarly debates on due diligence. These takeaways are presented as five propositions that seek to capture the common findings or areas of agreement in the scholarship under review, having regard to the existing case law of international courts and tribunals. These propositions are intended to inform our understanding of the nature, content and scope of application of due diligence and are, in principle, agnostic as to the particular field of international law. With an eye to the future, this essay has also singled out certain areas in the debate where scholars disagree or where the law may not yet be settled. Those areas may benefit from further research, practice of states, clarification by international courts and tribunals and, likely, codification by the International Law Commission in the near future. These efforts will go a long way towards ensuring that due diligence remains a well-circumscribed concept and thus useful to states and other participants in the international legal order.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.115 |
| Scholarly communication | 0.027 | 0.053 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.025 | 0.038 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".