Policy Improvement Measures to Revitalize Vacant House Management Projects : Focusing on the Cases of the UK, Japan, and Canada
Bibliographic record
Abstract
This study explores institutional improvement measures to enhance the effectiveness of vacant house management projects. The issue of vacant houses is intensifies, necessitating comprehensive overhaul in vacant house surveys, redevelopment planning, demolition and utilization, and information system operations. Through an in-depth review of domestic laws and systems, analyzing vacant house management initiatives in Chungcheongnam-do, and a comparative examination of best practices from the UK, Japan, and Canada, this study suggests key measures, including systematic surveys, efficient demolition and enforcement, tax incentives, special provisions for redevelopment, and expanded vacant house information systems. The study emphasizes the need for reforms based on practical issues identified on-site and explores ways to encourage private sector utilization of vacant houses. An integrated approach and continuous policy monitoring are essential to resolving the vacant house problem. Implementing the proposed measures is expected to contribute to longterm solutions and restore economic and social vitality to local communities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".