Bibliographic record
Abstract
Whilst the protection of civilians (POC) in conflict has been a recurring feature of the humanitarian discourse the same has not been true in military doctrines, where the protection of civilians has long been cast in terms of arms bearers upholding their responsibilities under international humanitarian law (IHL). However, opportunities for and pressures on military actors to develop more specific capacities and approaches in this field have grown: partly as a response to the changing nature, location and scope of conflict, particularly the increasing proportion of internal conflicts fought by irregular armed groups in urban environments. It is also a response to the scale and complexity of protection challenges in the Balkans, Rwanda, Darfur and Libya - each of which has clearly demonstrated that threats to civilians are complex and dynamic and that no single international actor is capable of mitigating them without significant support from other institutions (O’Callaghan and Pantuliano, 2007). Despite the enormous growth in opportunities for interaction between militaries and humanitarians there is only a very limited literature on their interaction over protection issues and evaluations of the emerging doctrines. Consequently this article charts the growth in military policies towards POC in the UN, UK, NATO and a range of other states as well as drawing attention to the challenges that still remain in operationalising responses.
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.098 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".