Inequality in the public priority perceptions of elected representatives
Bibliographic record
Abstract
Democratic representation presumes that politicians know what the public wants. Ideally, politicians have accurate perceptions not only of which policies citizens prefer (positions), but also of which issues citizens prefer to be dealt with first (priorities). How accurate are elites’ perceptions of the public’s priorities? And, if elite estimations are incorrect, is there inequality in these perceptions? Using data from two surveys – one measuring citizens’ priorities and one gauging representatives’ perceptions thereof – in Belgium, Canada and Israel, this article shows that politicians’ perceptions of the extent to which citizens want them to undertake action on various issues are not entirely accurate. Importantly, politicians’ perceptions appear to be biased towards the preferences of the male, highly educated, and politically interested citizens. These key findings apply to all three countries under study. When it comes to gender specifically, it is found that female politicians’ estimations are no less skewed towards male preferences than male politicians’ estimations, which suggests the skew is not the consequence of bad descriptive representation but rather of certain segments of citizens being more politically active. All in all, the results show that inequality in representation might partly be driven by underlying perceptual inaccuracy. Supplemental data for this article can be accessed online at: https://doi.org/10.1080/01402382.2021.1928830 .
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 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 teacher head, 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".