The Macro Polity Revisited
Bibliographic record
Abstract
This dissertation includes six articles tied together by the overarching question of how changes in public opinion, economics and public policy co-evolve in mature democracies, with a focus on redistributive (in seven European democracies) and secessionist preferences (in Catalonia and Scotland). The theoretical inspiration derives from three sources: 1. the Macro Polity model by Erikson, MacKuen/Stimson, 2. the Thermostatic Responsiveness model by Soroka and Wlezien, and 3. the literature on representation gap models by Gilens, Elsaesser and others. The Macro Polity and Thermostatic Responsiveness models come with an optimistic undertone, emphasizing that public policies adapt to public opinion, producing the policy-opinion congruence that defines responsive government. The Representation Gap model, by contrast, is more pessimistic in highlighting that the preferences of low-income groups are generally worse represented in public policies than the preferences of middle-income and especially high-income groups. While there is evidence in favor of these models for the majoritarian political systems in the US, Canada and the UK, less is known about the validity of these models in proportional democracies of continental Europe. The contributions in this dissertation address this research gap by integrating the three models and combining nearly 500 surveys to study the evolution of European public opinion at the national and subnational level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".