Politicians, Electoral Integrity, and Electoral Management Bodies: A Cross‐National Study on Satisfaction With Democracy
Bibliographic record
Abstract
Competitive elections that are free and fair are the bedrock of stable representative democracies. In this critical moment, in which there is an increase of democratic decline across states, it is imperative to (re‐)examine fundamental democratic processes, like elections. In addition to citizens, politicians are key actors in the electoral process. Politicians can influence the views of citizens, make changes within political institutions, and contribute to democratic breakdown or backsliding. Therefore, understanding their views about the way democracy works is crucial. While there has been a recent increase in the scholarship on politicians’ perceptions and behaviours, it has not yet considered whether aspects of the electoral process might affect politicians’ democratic satisfaction. Furthermore, while the literature on citizens’ democratic satisfaction is well‐established, our understanding of politicians’ satisfaction with democracy (SWD) is not. This article begins to address these gaps in the scholarship on SWD and politicians by examining whether electoral integrity and the characteristics of electoral management bodies influence politicians’ levels of SWD. By analyzing cross‐national data from The Comparative Candidates Survey covering 49 elections, in 21 countries, from 2005 to 2021, this article highlights three key findings: first, while electoral integrity affects levels of politicians’ SWD, it matters more for politicians who lost the election. Second, electoral management bodies’ independence does not affect politicians’ levels of SWD. Third, while electoral management bodies’ capacity influences politicians’ levels of democratic satisfaction, the strength of the effect differs for politicians on the ideological right and left. The implications of these findings are explained in the article.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".