FAKTOR-FAKTOR YANG MEMPENGARUHI KEBERHASILAN PENATAAN PKL SEBAGAI STRATEGI PENATAAN RUANG KOTA SURAKARTA
Bibliographic record
Abstract
Hunger and poverty countermeasure was appointed to be the first target in the Millennium Development Goals (MDGs) global mandate. It is relevant with Indonesia’s condition in the last three years in which the amount of poverty-stricken people grew significantly. To boost the population’s economy to a sustainable level, Solo’s City Government try to give more space to the informal sectors in the city. This policy doesn’t aim to improve the economy quantitatively but also equitably by facilitating the informal sectors, so that they can compete with the formal sectors that are dominated by the “big companies” as in nearly every big city in Indonesia. One of the growing informal sectors is the street vendors. Relocation and stabilization are the two programs run by the Surakarta government to give a better chance for the street vendors to survive the competition with the formal sectors. Hundreds even thousands of street vendors in Solo has been regulated to empower the city’s local economy. The factors that influence the success of the street vendor regulation isn’t only perceived from the city’s aesthetics, which always became the main reason, but also perceived from the quality growth in activity performance after the program has been done to ensure sustainability. The success of the street vendor regulation must be seen from the street vendor’s perspective. Unique character and street vendor behavior that vary richly must be known to ensure the street vendors can accept the planned program. In order to point out the location character role in the city’s spatial arrangement strategy, the focal point of this research is exploring the factors that influence street vendor regulation adjusting with the needs and demands of street vendors
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.041 |
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".