The Preference-Expectation Gap in Support for Female Candidates: Evidence from Japan
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Gender disparities in Japanese government are consistently high, but evidence of voter bias against female politicians is mixed. We argue that this discrepancy arises because some researchers measure Japanese voters’ first-order preferences (who they personally support) while other researchers measure Japanese voters’ second-order preferences (who they expect other voters to support). We call this gap between voters’ own preferences and expectations regarding others’ preferences the preference-expectation gap. Since this gap is a key mechanism of strategic discrimination, we test our argument using an experimental design modelled after research on strategic discrimination in the 2020 US Democratic primary elections. Based on two online conjoint survey experiments in Japan, our findings demonstrate the presence of a preference-expectation gap in Japanese public opinion on female politicians. Exploratory analyses of moderation effects reveal that female participants and those with more liberal views toward gender roles have larger preference-expectation gaps.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it