Affinity, Antipathy and Political Participation: How Our Concern For Others Makes Us Vote
Bibliographic record
Abstract
Abstract.Some citizens differ in their levels of concern for the supporters of various parties. I demonstrate how such concerns can motivate citizens to vote. I first present a simple formal model that incorporates concern for others and election benefits to explain the decision to vote. By predicting substantial turnout, this model overcomes the “paradox of participation.” I then verify the model empirically. I utilize a series dictator games in an online survey of more than 2000 Canadians to measure the concern of individuals for other partisans. I show how the preferences revealed in these games can predict the decision to vote in the face of several conventional controls. Taken together, the formal model and empirical results generate a more fulsome and satisfactory account of the decision to vote than an explanation which relies solely on duty. Résumé.Les citoyens ne se préoccupent pas tous des partisans des divers partis politiques. Je démontre comment de telles préoccupations peuvent motiver les citoyens à participer aux élections. Je présente d'abord un modèle formel qui explique la décision de voter en intégrant les préoccupations à l'égard des autres électeurs et les bénéfices associés à une élection. En prédisant une part substantielle de la participation, ce modèle surmonte le paradoxe de la participation électorale. Ensuite, le modèle est vérifié empiriquement. J'emploie à cette fin une série de jeux du dictateur insérés dans une enquête menée en ligne auprès de 2000 Canadiens afin de mesurer leur degré de préoccupation à l'égard des autres partisans. Je montre comment les préférences révélées dans ces jeux peuvent prédire la décision de voter. Ensemble, le modèle formel et les résultats empiriques produisent une explication plus éloquente et plus satisfaisante de la décision de voter lors d'une élection que les explications qui s'appuient seulement sur le sens du devoir.
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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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".