PENETAPAN ZONASI ATIFITAS PEDANGANG KAKI LIMA DI KOTA MEDAN STUDI KASUS KECAMATAN MEDAN PETISAH
Bibliographic record
Abstract
Belum adanya pengaturan kebijakan PKL yang jelas menyebabkan melubernya PKL dikawasan Pasar Petisah sampai ke badan jalan. Pasar Petisah menarik banyak Pedagang untuk berjualan disekitar jalan. PKL yang tidak tertata akan merusak kawasan Pasar Petisah. Hal inilah yang mendorong dirumuskan arahan penataan PKL di kawasan Pasar Petisah. Terdapat tiga metode analisis yang digunakan dalam studi ini, yaitu analisis Statistik deskriptif untuk mengetahui karakteristik PKL, kemudian analisisDelphi untuk menganalisis faktor-faktor yang berpengaruh terhadap penataan PKL dan menggunakan analisis deskriptif kualitatif untuk merumuskan arahan penataan PKL di kawasan Pasar Petisah. Hasil yang didapatkan dari penelitian berupa pengelompokan arahan berdasarkan aspek pembinaan, aspek lingkungan, asek manajemen lahan, aspek kebijakan serta aspek ekonomi. Berdasarkan arahan dengan mengkombinasikan peraturan yang ada maka akan didapatkan lokasi-lokasi yang diperolehkan pedagang melakukan kegiatan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".