The 2022 South Korean Presidential Election and the Gender Divide among the Youth
Bibliographic record
Abstract
The 2022 South Korean presidential election was the country's most closely contested election since a democratic direct electoral system was initiated in 1987, with less than a 1 percent di erence separating the two major candidates among 34 million votes cast. Despite some parallels with and continuities from previous elections, the 2022 election saw new voting alignments emerge based on one topic: gender equality. In this essay, we explain how and why gender became such a prominent issue during the 2022 election campaign, and how this a ected voting patterns, especially among male and female voters in their twenties and thirties. Specifically, we argue that gendered voter behaviour during the election arose from rising anti-feminist sentiments among young men, and that the two main presidential candidates politicized the issue to maximize support from this group. This in turn triggered the consolidation of a young female voting bloc. Using an original survey conducted in January 2022 with an approximate nationally representative sample of 1,017 respondents, we identify two possible causes of rising anti-feminist sentiments among young men: the belief that women receive preferential treatment in employment opportunities and mandatory military service for men. In addition, through an embedded survey experiment run before the election, we proposed that political candidates with pro-gender messages would be less likely to receive support from young men, while candidates with anti-gender messages would be likely to receive more support; these projections were confirmed by the actual voting breakdowns of the recent election. The results suggest that the new administration must handle gender issues with extreme care to ensure that divergent perceptions of the gender divide do not become further polarized over the next few years, since such a development could very well fuel democratic deconsolidation in South Korea.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.006 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".