Uncovering Inequalities in Food Accessibility between Koreans and Japanese in 1930s Colonial Seoul Using GIS and Open-Source Transport Analytics Tools
Bibliographic record
Abstract
This study aimed to investigate the disparities and inequalities in food accessibility in colonial Seoul (Keijo [京城] in Japanese, and Gyeongseong [경성] in Korean) in the 1930s, using a geographic information system (GIS) and open-source transport analytics tools. We specifically focused on the unique social standing of people in the colonial era, namely colonial rulers (Japanese) vs. subjects (Koreans) and examined whether neighborhoods with larger proportions of colonial rulers had more access to food opportunities. For a comprehensive evaluation, we computed food accessibility by multiple transport modes (e.g., public transit and walking), as well as by different time budgets (e.g., 15 min and 30 min) and considered various sets of food options—including rice, meat, seafood, general groceries, vegetables, and fruits—when measuring and comparing accessibility across neighborhoods in colonial Seoul. We took a novel digital humanities approach by synthesizing historical materials and modern, open-source transport analysis tools to compute cumulative opportunity-based accessibility measures in 1930s colonial Seoul. The results revealed that Japanese-dominant neighborhoods had higher accessibility by both public transit and walking than Korean-dominant neighborhoods. The results further suggest that inequality and disparity in food accessibility is observed not only in contemporary society but also in the 1930s, indicating a historically rooted issue.
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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 | 0.001 | 0.001 |
| 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".