Bibliographic record
Abstract
Imagine a time series of public preferences for policy in a particular area. Let us assume that we have regular annual readings of public preferences. To keep things simple, let us assume further that our readings are perfect; in other words, that there is no bias or sampling error. What happens when public preferences change? What are the consequences for policy? To what extent do changing policies then affect preferences? Our expectations are captured in a general model of opinion-policy dynamics. THE MECHANICS OF PUBLIC RESPONSIVENESS The representation of public opinion presupposes that the public actually notices and responds to what policymakers do. It means that the public must acquire and process information about policy, and adjust its preferences accordingly. As we have noted, without such responsiveness, policymakers would have little incentive to represent what the public wants in policy – without public responsiveness, expressed public preferences would contain little meaningful information. There not only would be a limited basis for holding politicians accountable; registered preferences would be of little use even to those politicians motivated to represent the public for other reasons. A responsive public will behave much like a thermostat (Wlezien 1995), adjusting its preferences for “more” or “less” policy in response to what policymakers do. When policy increases (decreases), the preference for more policy will decrease (increase), other things being equal. Consider the public as a collection of individuals distributed along a dimension of preference for policy activity, say, spending on defense.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".