Cross-segmental parties in consociational systems: Downplaying prowess to access power in Northern Ireland
Bibliographic record
Abstract
Political parties are afforded a key role in making consociational democracy work; however, parties that dis-identify with salient identities and appeal to voters across the ethno-political divide face barriers when interacting with voters and with other, segmental parties. Nevertheless, such cross-segmental parties often thrive and even ascend to power. Northern Ireland’s cross-segmental parties – the Alliance Party, the Green Party, and People before Profit – have sought to traverse group-specific voter interests and set their agenda apart from that of segmental parties. For such parties to be considered ‘coalitionable’, they should outline their (potential) governing contribution to complement other political parties’ agendas. Cross-segmental parties’ participation in government makes them appear electable, but it is the focus on bipartisan concerns that consolidates their electoral success and ensures their political relevance. We focus on the evolution of Alliance’s political agenda and fill a gap in the literature on the relevance of cross-segmental parties in consociations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".