Investigation of Urban Places in Seoul Digital Industrial Complex (G-Valley)
Bibliographic record
Abstract
The National Industrial Complex was designated by the Korean government to strategically nurture the country's key industries. It is larger than a normal industrial quarter, playing a major role in boosting national economic development. In Korea, industrial complexes began to be created since the post-war period. During the period, the country experienced the worst economic condition with many problems. Facing the problems, factories for light industry started to be established, then the focus has shifted towards heavy chemical industry [1]. Seoul Digital Industrial Complex (G-Valley) was established through the development of small to large manufacturing factories such as clothing workshops. However, because of the rapidly industrializing areas out of Seoul, a number of old factories moved to other locations in the 1980s and 90s, and G-valley was replaced by venture groups and IT related companies. Since its establishment in the 1960s, physical infrastructure has been aged and some social problems have been raised. In this paper, we will investigate the problems of G-valley and seek the future solutions by analyzing the current situation based on its urban spaces. It is significant to look at the common problems and future visions not only of G-valley but also of the industrial complexes in other areas of Seoul and its metropolitan region
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".