The costs of electoral fraud: establishing the link between electoral integrity, winning an election, and satisfaction with democracy
Bibliographic record
Abstract
Previous research has shown that voters' perception of electoral fairness has an impact on their attitudes and behaviors. However, less research has attempted to link objective measurements of electoral integrity on voters' attitudes about the democratic process. Drawing on data from the Comparative Study of Electoral Systems and the Quality of Elections Data, we investigate whether cross-national differences in electoral integrity have significant influences on citizens' level of satisfaction with democracy. We hypothesize that higher levels of observed electoral fraud will have a negative impact on evaluations of the democratic process, and that this effect will be mediated by a respondent's status as a winner or loser of an election. The article's main finding is that high levels of electoral fraud are indeed linked to less satisfaction with democracy. However, we show that winning only matters in elections that are conducted in an impartial way. The moment elections start to display the telltale signs of manipulation and malpractice, winning and losing no longer have different effects on voter's levels of satisfaction with democracy.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".