Does quality matter? An evaluation of the relationship between United Nations peacekeepers and civil war violence
Bibliographic record
Abstract
Why are some groups of peacekeepers more effective at managing civil war violence than others?Existing studies of operational effectiveness have focused on the quantity, profession, and geographic location of deployed personnel and have suggested that each of these is independently important.However, the relationship, if any, between the quality of UN peacekeepers and their ability to manage on-going violence remains critically understudied.To address this gap, this study begins by developing a novel definition of "peacekeeper quality" that consists of two key factors: professional capabilities and a willingness to act.It then evaluates the extent to which variation in peacekeeper quality contributes to operational effectiveness, at both the state-level and the local-level.I argue that higher quality peacekeepers are more effective than lower quality peacekeepers because they have an easier time coercing local conflict actors.To test this argument, I use an explanatory sequential methodology -a form of mixed-method research that uses qualitative data to help interpret the results of a primarily quantitative study.It starts with a cross-national analysis of all intrastate conflicts in sub-Saharan Africa from 1991-2017 to determine if variation in average operational quality affects the severity of violence at the state-level.It then proceeds to a geographically disaggregated analysis of three peacekeeping operations located in sub-Saharan Africa from 2010-2017 to determine if variation in the average quality of individual groups of peacekeepers affects the severity of violence at the local-level.Finally, it presents a process-tracing case study of MINUSCA's intervention in CAR from 2014-2018 that examines both overall trends and discrete conflict episodes.In short, this dissertation shows how and why peacekeeper quality matters.Specifically, it shows that the UN's highest quality peacekeepers are its iii most effective, that the quantity of peacekeepers continues to affect violence when controlling for quality and that peacekeepers are better at protecting civilians from harm than they are at managing battlefield violence.As a result, future attempts to evaluate the relationship between peacekeepers and violence, or to improve the UN's operational effectiveness, should also account for the role played by peacekeeper quality.
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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 | 0.016 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".