Job Accessibility as a Lens for Understanding the Urban Structure of Colonial Cities: A Digital Humanities Study of the Colonial Seoul in the 1930s Using GIS
Bibliographic record
Abstract
This study examined the urban structure of colonial Seoul in the 1930s, the capital city of Korea under the rule of the Japanese empire, by adopting quantitative geographical methods. We utilized a job accessibility index to operationalize the urban structure. We also used geographic information science (GIScience) analysis tools to digitize neighborhood-level sociodemographic and parcel-level business location information from historical materials. The results illustrated several findings that were not revealed by previous studies based on qualitative approaches. First, transit-based job accessibility (13.392) is significantly higher (p < 0.001) than walk-based job accessibility (10.575). Second, there is a Γ-shaped area with higher job accessibility, including the central part of colonial Seoul. Third, Japanese-dominant neighborhoods had significantly (p < 0.001) higher transit-based (27.156) job accessibility than Korean-dominant neighborhoods (9.319). Fourth, transit-based job accessibility is not significantly correlated with the unemployment rate overall. Although colonial Seoul was the seventh-largest city of the Japanese empire, few practical planning actions were taken to resolve urban issues, unlike the other large cities in mainland Japan.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".