How Citizens Want Their Legislator to Vote
Bibliographic record
Abstract
Different people have different views about what elected representatives should do in a democracy. Some people think legislators should follow their own conscience (personal view), others think they should do what the majority of citizens in their constituency want (view of the constituency), and yet others think they should do what they promised during the election campaign (campaign promise). Sometimes, these considerations converge, that is, the legislator is personally in favor of a proposed legislation, he or she promised to vote for that legislation in the previous election campaign, and there is majority support for it in the legislator's constituency. However, which of these consideration(s) should matter the most when there is a conflict? Using an experimental design, we ascertain how these principles of representation affect citizens' views about how legislators should vote on a salient policy (immigration). Of the three styles of representation, we find that citizens pay the greatest attention to the state of public opinion in their constituency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".