Taking Armed Groups Seriously: Ways to Improve their Compliance with International Humanitarian Law
Bibliographic record
Abstract
Abstract Most contemporary armed conflicts are not of an international character. International Humanitarian Law (IHL) applicable to these conflicts is equally binding on non-State armed groups as it is on States. The legal mechanisms for its implementation are, however, still mainly geared toward States. The author considers that the perspective of such groups and the difficulties for them in applying IHL should be taken into account in order to make the law more realistic and more often respected. It is submitted that the law is currently often developed and interpreted without taking into account the realities of armed groups. This contribution explores how armed groups could be involved in the development, interpretation and operationalization of the law. It argues that armed groups should be allowed to accept IHL formally, to create – amongst other things – a certain sense of ownership. Their respect of the law should also be rewarded. Possible methods to encourage, monitor and control respect of IHL by armed groups are described. The author suggests in particular that armed groups should be allowed and encouraged to report on their implementation of IHL to an existing or newly created institution. Finally, in case of violations, this contribution proposes ways to apply criminal, civil and international responsibility, including sanctions, to non-State armed groups.
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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 | 0.068 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".