Historicization of Homeless Spaces: The Seoul Train Station Square and the House of Freedom
Bibliographic record
Abstract
This paper historicizes "deserving" citizenship of South Korea by tracing spatial changes and meanings of two places: Seoul Train Station Square and a former textile factory renovated to a homeless shelter. Both have been emblematic space where the most homeless people were populated since the break of the Asian Debt Crisis. However, each place embodies different history of "deserving" citizenship in a complementary way. The square, a politically charged literary and physical topography, became a location of protecting "normal" citizen from potentially violent homeless people. The factory, a spatial marker for the state regulation of laborer, became a site to promote the benevolent image of welfare state for protecting homeless people through a demarcation of short-term street living people—as "deserving" homeless citizens—from long term street living people. The embedded history in two places would be the transition of developmental state towards the welfare state that shifted its capitalist state focus from labor/economic policies to welfare policy: neoliberalization of state governance in South Korea. In concrete, this paper examines how homeless people emerged as new welfare subjects in an urban landscape; how only short-term street living people were selected as proper; how various social agents were involved in the process of implementing homeless policies; and how dualistic capitalist control over labor power, such as regular workers and surplus laborer, was imposed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".