Bibliographic record
Abstract
A perennial question for students of democracy is the extent to which government policies align with voter preferences. This is often studied by comparing median voter opinion on a left–right scale with the cabinet weighted mean, that is, the mean left–right position of cabinet parties, weighted by their legislative sizes. Government positions may also be estimated from their declarations, however. In a recent investigation, McDonald and Budge found that declared government policy better accords with the voter median than with the cabinet weighted mean, a finding they interpreted as consistent with their hypothesis that actual government policy tends to reflect a “median mandate.” This investigation retests the McDonald–Budge model using a time-series cross-section methodology and an expanded data set. It finds no support for a median mandate interpretation but strong evidence that declared government positions respond to the positions of cabinet parties and, where present, external support parties. It also reveals a tendency for declared positions to be shifted to the right of the cabinet mean, a tendency that increases with the length of time that has elapsed since the last election (particularly for left-wing governments). This evidence that the policies governments set out to implement are systematically “right shifted” bears major consequences for our understanding of representative democracy.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".