Knives Out: Evolving Trends in State Interference with UN Peacekeeping Operations
Bibliographic record
Abstract
Abstract While peacekeeping operations have always been heavily dependent on host-state support and international political backing, changes in the global geopolitical and technological landscapes have presented new forms of state interference intended to influence, undermine, and impair the activities of missions on the ground. Emerging parallel security actors, notably the Wagner Group, have cast themselves as directly or implicitly in competition with the security guarantee provided by peacekeepers, while the proliferation of mis- and disinformation and growing cybersecurity vulnerabilities present novel challenges for missions’ relationships with host states and populations, operational security, and the protection of staff and their local sources. Together, these trends undermine missions’ efforts to protect civilians, operate safely, and implement long-term political settlements. This essay analyzes these trends and the dilemmas they present for in-country UN officials attempting to induce respect for international norms and implement their mandates. It describes nascent strategies taken by missions to maintain their impartiality, communicate effectively, and maintain the trust of those they are charged with protecting, and highlights early good practices for monitoring and analyzing this new operation environment, for reporting on and promoting human rights, and for operating safely.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".