Challenging the Westphalian Order: Incorporating Armed Groups in Law-Making Under International Humanitarian Law
Bibliographic record
Abstract
In recent times, much of the focus has been placed on the incorporation of certain non-state actors, such as NGOs and transnational corporations, into different lawmaking processes, although the resulting rules are considered soft law. However, little attention has been paid to the possibility of affording armed groups a degree of participation in law-making processes, in large part due to the argument that this might inappropriately legitimize such groups. Although it is not realistic for non-state armed groups (NSAGs) to fully participate in multilateral treaty-making processes, it will be argued that it is possible to include some of their views in the development of future humanitarian rules. In this paper, I will deal with four mechanisms through which armed groups could be included in law-making processes. Special consideration will be given to the Geneva Call Deed of Commitment in the case of Sudan as this provides an example of the way in which the commitment of an armed group to adhere to rules of international humanitarian law can influence the position of states in connection with ratifying treaties on IHL (in this case, the Ottawa Convention).
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".