How Does Winning, Losing, and Electoral Competitiveness Affect Voters’ Attitudes Toward Government? Evidence from Three Western Democracies
Bibliographic record
Abstract
Electoral competition has long been held as a required and essential component of a smoothly functioning democracy. This common wisdom suggests that representative responsiveness is likely to decrease as the level of general election competitiveness falls off. The closer the election the more uncertain the incumbent member of the legislature is about her ability to get reelected, which motivates her to work harder to win votes (i.e. campaign harder, spend more time in the district, secure more pork projects, modify voting behavior in the legislature, etc.). In turn, these activities should increase voter satisfaction and efficacy. This final linkage is what we take up here. Namely, do voters in districts that have competitive elections demonstrate higher levels of efficacy and higher levels of satisfaction with the representative and with the legislature itself? Using survey data from the most recent elections in three western democracies with single member district systems (UK, Canada, and the U.S.) we find no such connection. Rather, the winner/loser dichotomy is primal. Electoral competitiveness has no measurable effect on voters either in terms of satisfaction with their representative or with more abstract variables like efficacy. This finding has important implications for democratic theory and for redistricting.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".