Cultural sources of gender gaps: Confucian meritocracy reduces gender inequalities in political participation
Bibliographic record
Abstract
The historical oppression of women in China mirrors the experiences of women in many other cultures. During the imperial era, Confucian ideologies and institutions played a significant role in perpetuating women's disenfranchisement. This perception has become deeply entrenched, to the extent that contemporary discussions on women's status often automatically refer to lingering Confucian legacies as one of the obstacles to achieving gender equality. However, this study offers a nuanced perspective by shedding light on how certain aspects of Confucianism, notably meritocracy, may serve to empower modern-day women. The research focuses on the Chinese context, particularly the historically significant Confucian-based meritocratic institution known as the civil examination system ( keju ). I argue that historical meritocratic legacies can have a lasting impact on contemporary behavior, specifically by reducing the gender gap in political participation in local village elections. Using data from historical archives and the China General Social Survey, I find a negative correlation between a prefecture's historical success in the keju exams and the gender gap in village election turnout among present-day respondents. Further exploratory analyses reveal that the enduring Confucian tradition of meritocracy also empowers other low-status social groups, including the economically disadvantaged and the less educated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".