How we vote: information heterogeneity, process and choice
Bibliographic record
Abstract
Do more informed voters take account of a wider range of factors in formulating their vote choice? Do differences in the considerations employed in the vote calculus lead to different vote outcomes? Do political institutions and electoral complexity affect the relationship between information and the vote decision process and vote choice? Using survey data from the Australian, British, Canadian, New Zealand, and U.S. election studies (21 studies in total), this research tests how political information heterogeneity affects both the vote decision process and choice. In addition, this relationship is examined according to the complexity of the decision environment in which the decision is made. The primary proposition tested contends that information heterogeneity produces differences in the vote calculus that in turn lead to systematic and significant variation in vote choice. Secondly, this work tests the assumption that the magnitude of information effects will be a function of context complexity, with increased complexity resulting in greater information- based differences. The findings support the proposition that differences in political information lead to alternative decisions processes and vote outcomes. However, the difference in vote outcome across information cohorts does not increase with context complexity as expected. In fact, the opposite relationship is found. Under the most complex conditions vote choice differences between the most and least informed is nearly eliminated. This counter-intuitive finding is dissected using a series of simulations that compare actual vote choice to the choice individuals would have made had they all pursued the same decision calculus. The results reveal a significant divergence between high and low information vote choices given alternative decision processes that hold important implications for election outcomes.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".