Sandesh Sivakumaran,<i>The Law of Non-International Armed Conflict</i>
Bibliographic record
Abstract
The most common type of contemporary armed conflict is fought between a state and one or more armed groups, or between two or more armed groups, yet the law regulating non-international armed conflicts has been barely addressed by academics in comparison to the law on international armed conflicts. Moreover, in general, this field of law is analysed on a subsidiary basis, from the point of view of its divergences and gaps as to the latter, rather than systematically and independently. Sandesh Sivakumaran’s book is unique as it focuses solely on the law — understood broadly, not merely as comprising international humanitarian law — applicable to non-international armed conflicts and presents the way such conflicts are regulated as well as the specific rules that constitute this body of law. It thus treats the law of non-international armed conflicts as a whole, including international humanitarian law, international criminal law and international human rights law, which have been traditionally analysed separately. In addition, Sivakumaran clarifies — by adopting a historical perspective on various specific aspects of the regulation of armed conflicts throughout the book — the changes in perspectives and contributions brought by each of these bodies of law, including, but not limited to, humanitarian law applicable to international armed conflicts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".