Analysis of Factors Contributing to Involuntary Settlement of Single-Person Households Living in the Living Quarter Other than Housing Unit : Focused on the 2022 Survey on Residential Conditions in Living Quarter Other than Housing Unit
Bibliographic record
Abstract
The study aims to empirically examine the factors contributing to involuntary settlement among single-person households living in a living quarter other than a housing unit. The analysis utilized responses from 7,374 single-person households from the 「Survey on Residential Conditions in Living Quarters Other than Housing Unit」 conducted by the Ministry of Land, Infrastructure and Transport and the Korea Land and Housing Corporation (LH). Latent Class Analysis (LCA) was conducted based on the reasons for remaining in such housing (first, second, and third priorities) to derive their types, followed by multinomial logistic regression analysis to estimate the determinants for each type. These results suggest that the settlement of single-person households living in living quarter other than housing unit is not explained by a single factor, but rather is a multidimensional process formed by the overlapping of economic poverty, spatial inequality, and health and welfare vulnerability. Accordingly, policy responses require income and financial support to prevent payment overdues for those who are economically constrained; expansion of work-nearby public rental housing for those who are constrained by location and convenience; and integrated support for housing, medical care, and caregiving for those in unavoidable settlements. By introducing the concept of ‘involuntary settlement’ and empirically presenting the complex structure of housing vulnerability, this study is significant as it can contribute to the development of tailored housing welfare policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".