How Politicians (mis)Perceive Policy Salience
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 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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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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