Winning and Competitiveness as Determinants of Political Support<sup>*</sup>
Bibliographic record
Abstract
Objectives. This study examines the impact of competitiveness, winning, and ideological congruence on evaluations of democratic principles, institutions, and performance. We posit that winning matters most. Individuals will hold favorable views toward democracy when it produces the outcomes they desire, independent of other contextual factors associated with elections. Methods. We use cross‐sectional multiple regression models to analyze survey data from Australia, Canada, the United Kingdom, and the United States. Results. We find that the psychological effect of being an election winner at the national level greatly boosts evaluations of democracy, as measured with a host of different indicators, while competitiveness and congruence do not systematically affect these evaluations. Conclusions. This study sheds light on what factors boost regime support among the populace by sorting out the relative impact of being in a competitive district, winning (at the local and national level), and having a representative with a similar ideological outlook.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".