Modern warfare : armed groups, private militaries, humanitarian organizations, and the law
Bibliographic record
Abstract
The face of modern warfare is changing as more and more humanitarian organizations, private military companies, and non-state groups enter complex security environments such as Iraq, Afghanistan, and Haiti. Although this shift has been overshadowed by the legal issues connected to the War on Terror and intervention in countries such as Rwanda and Darfur, it has caused some to question the relevance of existing international humanitarian law. To bridge the widening gap between the theory and practice of the law, Modern Warfare brings together both scholars and practitioners who offer unique, and often divergent, perspectives on four key challenges to the law’s legitimacy: how to ensure compliance among non-state armed groups; the proliferation of private military and security companies and their use by humanitarian organizations; tensions between the idea of humanitarian space and counterinsurgency doctrines; and the phenomenon of urban violence. The contributors do not simply consider settled legal standards – they widen the scope to include first principles, related bodies of law, humanitarian policy, and the latest studies on the prevention and mitigation of violence. By bringing to light international humanitarian law’s limitations – and potential – in the context of modern warfare’s rapidly changing landscape, Modern Warfare opens a path to preventing further unnecessary suffering and violence. Modern Warfare is mandatory reading for academics and practitioners of international law and students and scholars of security studies, international relations, and political science. [From UBC Press | Modern Warfare - Armed Groups, Private Militaries, Humanitarian Organizations, and the Law, Edited by Benjamin Perrin]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".