PLACEMAKING RUANG JALAN KORIDOR KOMERSIAL KOTA SURAKARTA
Bibliographic record
Abstract
Placemaking is an attempt to form a space into a place that has meaning for its users. The concept of placemaking motivated by all the existing space in the city, both of street and public space that can have meaning as a place where it will occur at the site of social interaction and communication among users of space. Street in a commercial corridor in Surakarta is an important infrastructure to support commercial activities, but the function of the cause of street becomes passive and less interested in the community to conduct joint activities in that space. This shows that street is still not able to accommodate other activities simultaneously and social interactions that lead to placemaking. The problem in this study is whether placemaking already happening commercial corridor street and how much street placemaking commercial corridors. Scope of this research include the way to commercial corridor Surakarta with sample corridor commercial there are six . The method used to determine how much placemaking happened in the commercial corridor of street to be reviewed based on variables derived from placemaking elements, namely comfort and image, accessibility and linkage, uses and activities, as well as sosialibility. The research proves that placemaking has occurred in the commercial corridor of street in Surakarta. But the realization of placemaking in commercial corridor street is still weak, as indicated by the lack of availability of means of supporting activities in the form sitting group. Pedestrian walkway used for informal traders activity and social interaction has not been evenly occurs in the entirety of the commercial corridors in the city of Surakarta. Keywords: placemaking, street, commercial corridor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".