Political Knowledge and Enlightened Preferences: Party Choice Through the Electoral Cycle
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
This article adapts and tests the theory of enlightened preferences on two British electoral cycles: 1992–97 and 1997–2001. Using individual-level panel data, it extends previous work by explicitly incorporating the role of political knowledge. Its findings are generally very supportive of the theory. It is shown that knowledge of party platforms varies through both electoral cycles in a manner predicted by the theory; that is, it is highest immediately following election campaigns; these changes in political knowledge are closely mirrored by changes in the explanatory power of a model of party choice containing so-called ‘fundamental variables’ (i.e. socio-demographic and issue-related variables) as predictors. More specifically, fundamental variables do a much better job of accounting for party choice during election years than in mid-cycle. Finally, for all years of both panels a positive interaction is found between political knowledge and the ability of voters to match their issue preferences to party platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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