Beyond the core: Do ethnic parties ‘reach out’ in power-sharing systems?
Bibliographic record
Abstract
While power-sharing arrangements are often commended for establishing peaceful relations between major ethnic groups, they are also criticised for excluding ‘Others’. Nevertheless, more complex forms of party competition can emerge in power-sharing systems, including parties representing the dominant communities (‘ethnic parties’) seeking to engage Others (‘non-dominant’ groups). Drawing on semi-structured interviews with parties from Northern Ireland, we examine the extent to which dominant parties reach beyond their core ethnic constituencies, how and why. We consider increasingly salient non-sectarian issues, such as marriage equality and abortion, and explore how ethnic parties have sought to respond to these debates. We consider whether liberal forms of power-sharing influence the willingness of dominant parties to advance inclusion of non-dominant groups. Our findings suggest that under favourable conditions, flexible power-sharing can create space for incremental moves by ethnic parties to reach out to constituencies beyond their core, gradually moving the system towards more inclusive representation.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".