Introducing the UNCIPPO (UN Civilian Posts in Peacekeeping Operations) Dataset
Bibliographic record
Abstract
Abstract This research note presents a dataset on budgeted civilian personnel posts in UN peacekeeping operations by mission, unit, rank, and staff category in the 1991–2020 period: the UNCIPPO (UN Civilian Posts in Peacekeeping Operations) Dataset. Civilian staff in UN peacekeeping operations include specialists in political affairs, human rights, gender, child protection, electoral support, security sector reform, strategic communications, and information analysis, among others. Our coding of almost three hundred UN budget documents reveals what kinds of civilian posts member states agree to fund. UNCIPPO data also permit more nuanced analyses of the impact of civilian personnel on mission effectiveness. We illustrate this by re-examining Blair, Di Salvatore, and Smidt's (2023) study of the effect of civilian staff on host country democratization, showing that the observed effect is driven by international staff—countering a surprising negative national staff effect—and that staff in units with democracy-related tasks contribute more significantly to this effect than staff in other units. The dataset opens new avenues for research on peacekeeping operations (for example, on peacekeeping resourcing and effectiveness) and IOs more generally (for instance, on the politics of budgeting, the growth of transnational expertise, and the profiles of international bureaucrats).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".