Spatial Configuration of Key Parliamentary Constituencies in the Republic of Korea in the XXI Century
Bibliographic record
Abstract
This article analyzes electoral competition between key parties in the Republic of Korea. Although the electoral geography of RoK is atypical, the presence of an almost two-party system and the use of single-member districts allow for the analysis of competition in Korean elections in the same context as in more studied countries such as the United States, Canada, and New Zealand. We define the stably swing constituencies in 2000-2020’s, their location and factors of high volatility. The overwhelming majority of districts which regularly change their political orientation are located in the north of the country — Capital Region and Chungcheon — where high competition is observed in the largest cities. Electoral differentiation of urban agglomerations is complex and subject to the influence of multiple factors, but several patterns can be identified. A significant portion of swing constituencies are in this state due to their location in transitional zones between areas with high support for candidates from the conservative camp (center and far periphery of the city) and their opponents (suburban semi-periphery). The main factor of differentiation is the electoral divide between different generations of voters. In small cities, the electorate of the main parties balances each other out, resulting in these constituencies regularly changing their party orientation. Some constituencies in the center of Seoul have special significance for both parties, so political heavyweights traditionally run in them, attracting attention to their constituencies and making them swing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".