Power and ideology – A comparison of citizens’ and politicians’ satisfaction with democracy
Bibliographic record
Abstract
Abstract The scholarship on satisfaction with democracy has increased significantly in recent decades, with scholars investigating how democratic satisfaction influences political attitudes and behaviors as well as the individual and contextual determinants of citizens ’ satisfaction with democracy. To our knowledge, however, scholars omitted to examine the democratic satisfaction of politicians . Making use of survey data from the Comparative Candidates Survey, this research note addresses this gap in the literature. As a first step in this endeavor, we pose two objectives. First, we want to compare levels of democratic satisfaction across citizens and politicians in different countries to evaluate whether mass-elite gaps are apparent. Second, we want to replicate core findings from the research on citizens but with politicians. As such, we examine two hallmark findings in the literature on democratic satisfaction with respect to the role of ideological extremism/nicheness and the winner-loser gap at elections. Our study contributes to the growing literature on elites’ attitudes and behaviors and identifies some of the conditions that favor and undermine politicians’ satisfaction with democracy. This is a crucial research endeavor given elites’ influence on public opinion and democratic stability.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".