Bibliographic record
Abstract
The correspondence between public preferences and public policy is a critical rationale for representative democratic government. This view has been put forward in the theoretical literature on democracy and representation (e.g., Dahl 1971; Pitkin 1967; Birch 1971) and in “functional” theories of democratic politics (Easton 1965; Deutsch 1963), both of which emphasize the importance of popular control of policymaking institutions. Political science research also shows a good amount of correspondence between opinion and policy, though to varying degrees, across a range of policy domains and political institutions in the U.S. and elsewhere. This is of obvious significance. Earlier versions of this paper were presented at the 2006 Annual Meetings of the American Political Science Association, Philadelphia, at the Elections, Public Opinion and Parties specialist group, Nottingham, England, and at the 2007 National Conference of the Midwest Political Science Association, Chicago. We thank Vinod Menon for assistance with data collection and Kevin Arceneaux, Suzie DeBoef, Harold Clarke, Peter Enns, Mark Franklin, Martin Gilens, John Griffin, Will Jennings, Rich Joslyn, Benjamin Page, David Sanders, David Weakliem, John Zaller, and the anonymous reviewers for comments.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".