Travel tips for students of electoral choice: The dynamics of partisanship in Britain and elsewhere
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For more than half a century, party identification has been one of the most important concepts in studies of electoral choice. The concept has been the subject of an enormous amount of research, but protracted, unresolved, controversies remain. One of the most theoretically important of these debates concerns the extent to which voters’ partisan orientations manifest individual- and aggregate-level instability. In this chapter, we discuss the origin of the concept of party identification and ensuing controversies concerning the stability of voters’ partisan attachments. Next, we use data from the British Election Studies and other sources to investigate the dynamics of partisanship in Great Britain. Then, data from panel surveys conducted in Canada, Germany and the United States are employed to place the British findings in comparative perspective. The conclusion reprises major findings and briefly discusses their implications for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it