The international mediation of power-sharing settlements
Bibliographic record
Abstract
Power sharing is largely accepted among scholars and policy-makers as a potentially effective mechanism for building peace in the aftermath of violent ethnic conflicts and self-determination disputes. Although the operation of power sharing may be prone to ongoing challenges and even political crises arising from the legacy of the conflict, international actors continue to promote power-sharing arrangements to manage self-determination and other ethnopolitical conflicts. This article investigates the normative and instrumental reasons why third-party mediators (on behalf of international organizations and/or states) turn to power-sharing strategies during peace negotiations. It considers the reasons why third-party mediators promote power sharing when its maintenance is likely to depend on their ongoing commitment and governance involvement. We argue that mediators draw from four different perspectives in their support of power-sharing settlements: international law, regional and internal security, democracy and minority rights, and a technical approach where mediators focus on the mechanics of power-sharing designs. The article draws on in-depth semi-structured interviews with officials from the United Nations and the European Union working for the organizations’ respective mediation units as well as documentary analysis of official mediation documents.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".