Party identification in the wake of the crisis: a nascent realignment?
Bibliographic record
Abstract
Many commentators have sounded the death knell for party identification. For example, Dalton claims that we are witnessing a general process of partisan dealignment and that this trend ‘reflects long term and enduring characteristics of advanced industrial societies’ (Dalton 2002, p. 29). Like many other countries, Ireland experienced a sustained period of political dealignment, beginning in the 1970s (or earlier) and continuing right through to the new millennium. In Eurobarometer polls taken in the late 1970s, approximately two thirds of Irish respondents described themselves as being close to a political party; this had declined to 40 per cent by the mid-1990s (Mair and Marsh 2004, 242). As reported below, just over one quarter of respondents admitted to feeling close to a party in Irish National Election Study (INES) surveys conducted in 2002 and 2007, and this fell even further in in 2011
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".