A Geopolitical Economy of Heavy Industrialization and Second Tier City Growth in South Korea: Evidence from the ‘Four Core Plants Plan’
Bibliographic record
Abstract
In this article, we examine heavy industrialization and second tier urbanization in South Korea during the 1970s from a geopolitical economic perspective. We highlight the crucial, spatially complex geopolitical process of forming transnational class alliances, embedded in Cold War geopolitics, which has been neglected within state-centric developmental state theories and approaches to urbanization. Specifically, we trace the changes in the state’s original developmental plan for promoting the machinery industry in the southeast region during the 1960s and 1970s. We show how Hyundai, one of the most dominant chaebols, grew to exercise decisive influence over the state’s developmental strategy and became a powerhouse in the Korean economy, particularly in the city of Ulsan. Based on a case study of the Four Core Plants Plan, we show that the success of Hyundai was not an outcome of the effectiveness of the state’s developmental policy but was, ironically, due to the failure of the government’s original plan. The successful substitution of Hyundai’s own strategy for the state’s plan, which contributed enormously to the growth of Ulsan, would have been impossible without Hyundai’s enrollment into the transnational geopolitical economic alliance spurred by US military projects in Asia.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".