Crisis managers but not conflict resolvers: Mediating ethnic intrastate conflict in Africa
Bibliographic record
Abstract
Instability and conflict within African countries are on the rise. What are the best means for third parties to promote short-term crisis management and long-term conflict resolution in these situations? Often, these two tasks are at odds with one another, and certain approaches to intervention may be more or less effective. This study grapples with these issues by focusing on one particularly difficult set of cases—violent crises that are rooted in ethnic divisions and are part of protracted conflicts in Africa during the post-Cold War era—and one approach to intervention—mediation. We also view mediation as a multidimensional strategic process, and we test a series of hypotheses linking specific mediation styles to various crisis outcomes. The data and analyses reported in this study grew out of a new project named Mediating Intrastate Crises that is focused on uncovering the dynamics of successful mediation efforts during crisis situations at the intrastate level, which are important but understudied phenomena. Our findings indicate that mediators are highly effective at managing crises in the short term, particularly when they adopt a more intrusive approach. However, they have insignificant effects on long-term conflict resolution, showing little ability to stem the tide of recurrent violence.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".