How power-sharing includes and excludes non-dominant communities: Introduction to the special issue
Bibliographic record
Abstract
As the introduction to the special issue titled Democratisation in Divided Places: Designing Power-Sharing Institutions for Broad Inclusion, this article situates the themes, issues, and findings of the issue in a broad disciplinary perspective. Drawing from theories of constitutional design, peacebuilding, democratisation, and ethnonational accommodation, the article outlines the trade-offs that power-sharing faces in war-to-peace transitions and the implications for non-dominant groups. We articulate what we see as a central problem with contemporary power-sharing arrangements, a phenomenon we call the ‘exclusion amid inclusion’ dilemma. That is, for power-sharing to create stability and pacify the dominant groups, it must marginalise non-dominant groups. These are groups who were neglected in the original design of power-sharing institutions, who remain on the sidelines of postconflict politics, and who face major institutional constraints on their representation and participation in the power-sharing arrangement. Using ‘exclusion amid inclusion’ as an analytical lens, we explain how the articles included in the special issue highlight how different societies have grappled with the question of facilitating broad inclusion in the design of political power-sharing 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".