TRANSFORMASI SPASIAL PADA KORIDOR RUANG JALAN CENDRAWASIH – DEMANGAN BARU YOGYAKARTA
Bibliographic record
Abstract
Spatial transformation in corridors road space is a shape transformation of the road space structure and shape from the previous condition to the current condition, including physical and non-physical spaces, namely roads and the scope of road space as a form of a spatial structure. The Cendrawasih – Demangan Baru corridor road space has a strategic location, which is in Depok District as the center of education and economy in Sleman Regency and it is directly next to the Yogyakarta city. It has a direct impact on the Cendrawasih – Demangan Baru corridor road space so it be through a spatial transformation due to the increase of commercial activity in this area. This study used a qualitative rationalistic method with comparative descriptive analysis. This study examined the spatial transformation of the Cendrawasih – Demangan Baru corridor road space from 2006 to 2021 by combining field observations, interviews, and literature studies. Based on the analysis and theoretical study results, there was a significant transformation in the scope of the road space which included a partial transformation to a total transformation, while the roads and pedestrian paths had not changed in their dimensions, but both of them could not fulfill the needs of road space users due to the transformation in the road space scope. The factors causing the transformation were function transformation, ownership transformation and building transformation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".