Protecting Civilians during the Fight against Transnational Terrorism: Applying International Humanitarian Law to Transnational Armed Conflicts
Bibliographic record
Abstract
Summary This article explores how international humanitarian law (IHL) may apply to protect innocent civilians during the fight against transnational terrorism. To achieve the goal of allowing states to protect their populations from the threat of terrorism while respecting the rule of law and the rights of individuals, it is argued that, while IHL should remain applicable only to armed conflicts it must evolve so that it clearly applies to “transnational” armed conflicts (that is, armed conflicts between State A and a non-state actor based in State B, where State A uses force in the territory of State B without State B’s consent). Rather than recognizing a new third category of armed conflict to cover these situations, it is argued that non-international armed conflicts should be understood as a residual category that regulates all armed conflicts to which the parties are states and/or their agents.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".