A Study on the Master Planning of the Sustainable Global Contents City for the Redevelopment of Daegu K-2
Bibliographic record
Abstract
The purpose of this study is to propose and critically assess a sustainable urban regeneration model for the redevelopment of the former K-2 military airbase in Daegu, Korea. Large-scale idle military sites pose significant challenges in terms of ecological remediation, social integration, and economic transformation, but also offer opportunities for redefining urban identity and global competitiveness. To address this, we develop the concept of the “Global Contents City,” a planning framework that integrates cultural exchange, creative industries, education, and tourism within a sustainable urban ecosystem. The research employs a qualitative methodology that combines theoretical review, comparative analysis of international precedents (e.g., Munich-Riem, Tempelhof, Stapleton, and Toronto), and design-oriented masterplanning. The findings highlight design strategies that spatially interconnect cultural, educational, industrial, and ecological functions while reinforcing low-carbon infrastructure and green open space. By situating the Daegu K-2 case in an international context, the study demonstrates how lessons from post-military redevelopments can be adapted to Korea, contributing to both scholarly debates and practical frameworks for sustainable city-making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".