Does Peacekeeping Reduce Violence? Assessing Comprehensive Security of Contemporary Peace Operations in Africa
Bibliographic record
Abstract
Quantitative research evaluating the effect of peacekeeping operations usually links conflict abatement to the number of casualties in order to measure mission success. Such an approach is incomplete as security concerns extend far beyond the number of conflict related deaths. This narrow understanding of mission success leaves a significant assessment gap. Therefore this study is the first which presents comprehensive data using a wider understanding of violence and peace. We apply 11 indicators measuring security comprehensively. These range from the number of battle death, to violence against civilians, domestic unrest as well as domestic governance and political stability. In contrast to the mainstream quantitative literature our analysis shows that conflict often persists even with the deployment of peacekeepers. The absence of war (decline of battle death) does not automatically equate for non-violence and peace. In order to explain variation between cases we are also exploring the significance of different peacekeeping types, the size of developmental aid, rents from natural resources and the role of governance on conflict.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".