Bibliographic record
Abstract
Abstract Many analyses look at protection of civilians in UN peacekeeping from 1999 onwards. However, the issue has a longer genealogy. There have been four advocacy episodes: an unsuccessful persuasion attempt by the Secretary-General during the 1960s mission in the Congo, incoherent advocacy by proponents of the ‘safe areas’ policy in Bosnia, a partially successful campaign by elected Security Council members during the Rwandan genocide, and successful persuasion by Canada during its 1999–2000 Council term. Focusing on the (partially) successful episodes, a coalition of elected Council members used a threat of shame to extract concessions from the permanent members to adopt a presidential statement critical of the Rwandan government. They faced a mix of advantageous conditions, including advocates’ reputation, post–Cold War unity, credibility of the private threat, and a cultural match, as well as inauspicious circumstances, such as high issue salience and targets’ counter-narratives. In 1999, Canada used persuasion to place protection of civilians on the Council’s agenda. Favourable circumstances included advocates’ skill, targets’ first-hand exposure to civilian suffering, a cultural match, repeated interactions, a crisis of peacekeeping, and a private setting. In 2010, the Secretariat produced a concept of protection of civilians, followed by policy and guidance. Missions nowadays have units or coordination forums on the issue, and the Protection of Civilians Team exists at headquarters. In the late 2010s, protection became a priority in several missions, yet contestation by traditional sceptics, such as Russia, as well as major troop contributors and UN officials, also intensified.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".