The costs of electoral fraud: establishing the link between electoral integrity, winning an election, and satisfaction with democracy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it