Bibliographic record
Abstract
Abstract Impartiality is a central norm in United Nations peace operations, a guiding principle that has helped legitimize multilateral intervention in dozens of armed conflicts around the world. In practice, it has long been associated with passive monitoring of cease-fires and peace agreements. In the twenty-first century, however, its meaning has been stretched to permit a range of forceful, intrusive, and ideologically prescriptive practices, all in the name of building durable peace. How did this happen? Intrusive Impartiality draws on original interview and archival data to explain how new ways of practicing impartiality emerge, spread, and become institutionalized in UN peace operations. It shows that new ways of practicing impartiality are products of contestation, learning, and the interplay between top-down pressures and bottom-up drivers of change in UN peace operations. These findings challenge top-down theories of change in international organizations by foregrounding the creativity and agency of the field staff who are responsible for translating mandates into action. Intrusive Impartiality explains why peacekeepers approach their work differently based on past experience and it shows how they adjust their day-to-day practices to suit local circumstances. The book also demonstrates that they are continuously learning from each other in real time. Combining insights from practice theory and the literature on norm contestation, Intrusive Impartiality provides an innovative framework for studying authority and change in global governance. In doing so, the book sheds light on controversial changes in peacekeeping practice and yields important insights for policymakers about leadership, staffing, hiring, and training for UN peacekeepers.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".