Predictive Peacekeeping: Strengthening Predictive Analysis in UN Peace Operations
Bibliographic record
Abstract
The UN is becoming increasingly data-driven. Until recently, data-driven initiatives have mainly been led by individual UN field missions, but with António Guterres, the new Secretary-General, a more centralized approach is being embarked on. With a trend towards the use of data to support the work of UN staff, the UN is likely to soon rely on systematic data analysis to draw patterns from the information that is gathered in and across UN field missions. This paper is based on UN peacekeeping data from the Joint Mission Analysis Centre (JMAC) in Darfur, and draws on interviews conducted in New York, Mali and Sudan. It will explore the practical and ethical implications of systematic data analysis in UN field missions. Systematic data analysis can help the leadership of field missions to decide where to deploy troops to protect civilians, guide conflict prevention efforts and help preempt threats to the mission itself. However, predictive analysis in UN peace operations will only be beneficial if it also leads to early action. Finally, predictive peacekeeping will not only be demanding of resources, it will also include ethical challenges on issues such as data privacy and the risk of reidentification of informants or other potentially vulnerable people.
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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 | 0.188 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.022 | 0.038 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".