The Simplest Shortcut of All: Sociodemographic Characteristics and Electoral Choice
Bibliographic record
Abstract
Voters' decision criterion of last resort is their similarity to candidates or party leaders. Most normative theories would denigrate this form of reasoning. But the recent argument that voters can make up for information shortfalls by employing heuristics seems to require that the most poorly informed respond to these characteristics if they are to make anything other than a random decision. In this article I test the hypothesis that increasing dissimilarity of sociodemographic characteristics from a political figure (e.g., party leader) decreases a voter's expected utility from the election of that person. Secondarily, I ask whether decreases in a voter's store of policy information will necessitate greater reliance-a tendency to "fall back"-on this similarity/dissimilarity criterion. I draw on survey data from two Canadian federal elections with adequate variation in party leader characteristics. A model of vote choice is estimated by conditional logit. All voters are found to respond negatively to increasing sociodemographic distance from party leaders, net of partisanship, economic retrospections, policy, and uncertainty. Voters equipped for policy voting do not ignore these characteristics, and voters without policy information do not respond more strongly to their similarity or dissimilarity to party leaders.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".