Citizen endorsement of contested peace settlements: public opinion in post-Dayton Bosnia
Bibliographic record
Abstract
This article asks what shapes public support for comprehensive peace agreements that aim to end violent conflict in deeply divided societies. Although public perceptions are critical for the success of territorial settlements, little scholarly attention has focused on citizen attitudes towards peace in the post-implementation phase. We develop and empirically test a theory of citizen support for peace agreements that relates power-sharing institutions to broader citizens’ security considerations and integrates into this theory the roles of international peacekeeping, ascriptive minority/majority identities and exposure to alternative governance structures (i.e. federal, consociational and centripetalist). We argue that the degree to which a peace treaty provides credible security to citizens determines public support for power-sharing. We test this argument in a least likely case Bosnia Herzegovina. Using data from a 2013 representative survey with 1007 respondents, we examine the determinants of popular support for the Dayton Peace Accords. Our findings suggest that in each of the three main ethnic groups of Bosnia, more people would have voted for Dayton than against it and highlight the mechanisms through which individual and ethnic group security concerns shape support for the country’s post-conflict institutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".