Implementation in practice: The use of force to protect civilians in United Nations peacekeeping
Bibliographic record
Abstract
Since the failures of the United Nations of the early 1990s, the protection of civilians has evolved as a new norm for United Nations peacekeeping operations. However, a 2014 United Nations report found that while peacekeeping mandates often include the use of force to protect civilians, this has routinely been avoided by member states. What can account for this gap between the apparently solid normative foundations of the protection of civilians and the wide variation in implementation? This article approaches the question by highlighting normative ambiguity as a fundamental feature of international norms. Thereby, we consider implementation as a political, dynamic process where the diverging understandings that member states hold with regard to the protection of civilians norm manifest and emerge. We visualize this process in combining a critical-constructivist approach to norms with practice theories. Focusing on the practices of member states’ military advisers at the United Nations headquarters in New York, and their positions on how the protection of civilians should be implemented on the ground, we draw attention to their agency in norm implementation at an international site. Military advisers provide links between national ministries and contingents in the field, while also competing for being recognized as competent performers of appropriate implementation practices. Drawing on an interpretivist analysis of data generated through an online survey, a half-day workshop and interviews with selected delegations, the article adds to the understanding of norms in international relations while also providing empirical insights into peacekeeping effectiveness.
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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 | 0.045 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".