How costly is voting? Explaining individual differences in the costs of voting
Bibliographic record
Abstract
A more profound analysis of the variables in the voting equation is needed to improve our knowledge on voting. In this paper, we endogenize the costs of voting (C) and test several models with the help of the Making Electoral Democracy Work database, which contains information on C and its potential determinants for national elections in France, Germany, Spain, Switzerland, and Canada. We test whether C is affected by socio-demographic and attitudinal factors related to: (a) informational costs, (b) the costs of the act of voting itself, and (c) those bound to lead to an ex-ante rationalization of C. By doing so, we contribute to bridging the rational choice and the sociological and psycho-sociological models of voting. We find strong evidence that the three types of factors have a statistical and substantive effect on C. In particular, C falls with party identification, education, union membership, years in the region where the elections are held, interest in politics and the importance attributed to elections, while it is higher for women and rural dwellers. Age shows a curvilinear relationship, initially reducing C and increasing it later. Contrary to expected, the presence of kids at home does not significantly increase C.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".