Bibliographic record
Abstract
Abstract Long viewed as an example of effective multilateralism, UN peace operations are facing mounting challenges. Transformations in the landscape of conflict are outpacing their ability to respond. Rising expectations of peacekeeping have led to disenchantment with what they can deliver, while dis- and misinformation tactics undermine the efforts of the UN to make and build peace. As UN peace operations risk becoming another casualty of intensifying international tensions, great power rivalry, and the erosion of the rules and norms that govern international cooperation, we consider the future of UN peace operations. In the debate between a “pragmatic” and an “adaptive” approach to peacekeeping, we argue that a fundamental question is the ability of both alternatives to address three recurring issues that have shaped the effectiveness and legitimacy of peace operations: the mismatch between ambitious mandates and limited resources; the gap between the protection of civilians objective and its implementation in practice; and growing difficulties in honoring the principles of impartiality. We argue that policymakers and researchers should not lose sight of the fact that peacekeeping's legitimacy depends on its adherence to some version of host-state consent and some kind of restriction on when and how force is used. The expectation of civilian populations that the UN stands for protection also means that the UN must continue to safeguard some key norms associated with peacekeeping.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".