Bibliographic record
Abstract
International humanitarian law (IHL) is a set of rules which seek, for humanitarian reasons, to limit the effects of armed conflict. It protects persons who are not or are no longer participating in the hostilities and restricts the means and methods of warfare. IHL is also known as the law of war or the law of armed conflict. In the aftermath of 9/11, IHL has begun to resonate more widely with students and faculty as a subject of relevance and interest at law schools throughout the United States. Many topics related to this important branch of law - such as treatment of persons detained due to armed conflict, as highlighted by the revelations of abuse at Abu Ghraib - have emerged at the forefront of debate and learning in academic circles and national discourse. Yet coverage of IHL in U.S. law schools is still limited and, while interest is growing, many schools have not actively or systematically accommodated that interest. In the fall of 2006, American University Washington College of Law Center for Human Rights and Humanitarian Law (WCL) and the International Committee of the Red Cross (ICRC) Regional Delegation to the U.S. and Canada partnered to conduct research to assess the extent to which IHL is currently taught at accredited law schools in the United States, if at all, to gauge the level of interest in IHL and to identify specific ideas to increase coverage of the subject.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".