Only as fast as its troop contributors: Incentives, capabilities, and constraints in the UN’s peacekeeping response
Bibliographic record
Abstract
Abstract International organizations’ ability to respond promptly to crises is essential for their effectiveness and legitimacy. For the UN, which sends peacekeeping missions to some of the world’s most difficult conflicts, responsiveness can save lives and protect peace. Very often, however, the UN fails to deploy peacekeepers rapidly. Lacking a standing army, the UN relies on its member states to provide troops for peacekeeping operations. In the first systematic study of the determinants of deployment speed in UN peacekeeping, we theorize that this speed hinges on the incentives, capabilities, and constraints of the troop-contributing countries. Using duration modeling, we analyze novel data on the deployment speed in 28 peacekeeping operations between 1991 and 2015. Our data reveal three principal findings: All else equal, countries that depend on peacekeeping reimbursements by the UN, are exposed to negative externalities from a particular conflict, or lack parliamentary constraints on sending troops abroad deploy more swiftly than others. By underlining how member state characteristics affect aggregate outcomes, these findings have important implications for research on the effectiveness of UN peacekeeping, troop contribution dynamics, and rapid deployment initiatives.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".