How Politicians (mis)Perceive Policy Salience
Bibliographic record
Abstract
For representation to work well, elected politicians need to have a good grasp not just of which policies citizens support but also of which policies are most salient to citizens. Recent studies have revealed that elected politicians are poor at estimating levels of support for different policies. However, little is previously known about whether politicians are able to judge the salience of policies among citizens. In this study, we take on this task using data from four different countries on politicians’ estimations of the salience of different policies. We find that politicians routinely under-estimate the salience of policies to citizens, and are most likely to under-estimate the salience of a policy when doing so reduces their cognitive dissonance, either because they themselves think a policy is of less significance or because they perceive their own positional preferences to be incongruent with citizens’ preferences. The results demonstrate how motivated reasoning affects politicians’ judgements of citizens’ priorities and highlight a possible cause of voters’ dissatisfaction with the responsiveness of governments and politicians.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".