Gender Quotas and Candidate Selection Processes in South Korean Political Parties
Bibliographic record
Abstract
South Korea is one of the few East Asian countries in which candidate gender quotas are legislated for all levels of government. However, the implementation of quotas has been only partially successful as political parties do not comply with quota laws in the majoritarian tier of the country’s mixed-member electoral system. To explain this non-compliance, this article examines how Korea’s party organizations and candidate selection practices have subverted quota implementation. More specifically, we employ Rahat and Hazan’s framework that disaggregates candidate selection processes into four areas—the selectorate, candidacy, centralization, and voting vs. appointment—and examine how two major Korean parties have chosen their candidates in the last three elections. By doing so, we demonstrate that in Korea’s under-institutionalized parties, where party organizations have been overshadowed by individual personalities, implementation of quotas can easily be subordinated to the clientelistic incentives of party leaders. While the parties’ centralized and exclusionary candidate selection procedures give party leaders a great deal of latitude to implement quotas, a better gender balance in the set of candidates is rarely a top priority for leaders in parties where personalism prevails. We argue that this explains why the quotas in Korea have been ineffective.
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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 | 0.005 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".