Gender, engineering, and innovation in South Korea: an empirical investigation of the industrial patriarchy of the Southeastern region
Bibliographic record
Abstract
This study examines the role of gender in the industrial development and innovation of South Korea’s Southeastern heavy manufacturing clusters through the concept of ‘industrial patriarchy.’ The term denotes institutional practices and norms historically rooted in the 1970s and 1980s that reinforced gendered patterns of education and employment in the region’s industrial cities. The study examines gender marginalisation in regional innovation policies and workforce segregation, utilising text mining, policy document analysis and regression analyses. The results show that women are systematically excluded from engineering employment and from policy discourses on regional innovation, which, in turn, sustains gender segregation in education and work. At the same time, firm-level regression analyses indicate that women’s share of the workforce has a positive effect on innovation. Thus, the persistent under-representation of women in the heavy industrial sectors of the Southeastern region has negative consequences for regional innovation performance. By highlighting how gender, engineering and territorial context interact, these findings provide anuanced understanding of the structural barriers shaping industrial transformation and contribute new evidence to debates on the gendered dynamics of innovation in East Asian manufacturing regimes.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".