Party images in Northern Ireland: evidence from a new dataset
Bibliographic record
Abstract
The literature on belief systems in mass publics shows that survey respondents typically have difficulty in describing their images of political parties; only about half offer a meaningful description of how they see individual parties. This paper investigates what people in Northern Ireland think that parties stand for in their home jurisdiction, in Great Britain and in the Republic of Ireland, using open-ended questions in a survey of 1,008 Northern Ireland residents. Northern Ireland respondents resemble those elsewhere, in that only about half seem able to offer a politically meaningful description of what local parties stand for. Among the more politically sophisticated, the Northern Ireland parties are described in ethnonational terms, the British parties are placed in socio-economic (social class and left-right) categories, but few respondents know how to describe the parties in the Republic of Ireland. There is an intriguing asymmetry in the characterization of Northern Ireland’s unionist and nationalist parties: the DUP emerges as only marginally more ‘hard-line’ than the UUP, whereas a great gulf exists between the SDLP and Sinn Féin, the former being perceived as much more moderate. Notwithstanding high levels of electoral stability in Northern Ireland, our findings show that party supporters vary greatly in their levels of political sophistication, perhaps allowing elites greater freedom of action than if all voters were highly politically informed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".