The Warping of International Humanitarian Law: Problematizing Regime Interaction in the Law of Armed Conflict
Bibliographic record
Abstract
This essay has two aims. The first is to demonstrate that substantive international humanitarian law (IHL) is being ‘warped’ in its application through the prisms of international human rights law (IHRL) and international criminal law (ICL), i.e. IHL's substantive norms are being reinterpreted by IHRL and ICC courts and institutions. 'Warping' can undoubtedly create normatively desirable outcomes that improve IHL doctrine without upsetting its internal principles, in particular the balance between humanity and military necessity. However, there are also many instances of warping where IHL is made weaker. The central argument of this essay is that the outcomes of warping are inherently uncertain, and this uncertainty in the development of IHL norms, and especially uncertainty as to what the positive law is, constitutes a significant threat to IHL compliance. The second aim of this essay is to outline potential ways forward to address the risks of the warping of IHL. Firstly, warping occurring at the doctrinal level might be avoided through a theory of ‘compound norms’ accounting for the composite nature of the norms applied by ICL and IHRL institutions: they contain both IHL and IHRL/ICL elements resulting in a compound different from its component parts. Secondly, warping at an institutional level might be prevented through more formalised cooperation between law of armed conflict institutions.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".