PENGATURAN PEDAGANG KAKI LIMA (PKL) DI LINGKUNGAN TEGALBOTO BERDASARKAN PERATURAN DAERAH NOMOR 6 TAHUN 2008 KABUPATEN JEMBER
Bibliographic record
Abstract
Abstrak Gangguan ketertiban dan kebersihan di wilayah perkotaan Kabupaten Jember adalah diantaranya disebabkan keberadaan Pedagang Kaki Lima (PKL)yang menempati area trotoar dan bahu jalan, sehingga mengakibatkan pejalan kaki dan kendaraan yang melintas terganggu.Tujuan penelitian ini adalah untuk mengetahui pengaturan dan penataan Pedagang Kaki Lima (PKL) yang ada di kota Jember berdasarkan Peraturan Daerah Nomor 6 Tahun 2008. Penelitian ini menggunakan metode kualitatif deskriptif, membahas efektivitas Peraturan Daerah Nomor 6 Tahun 2008 tentang Pedagang Kaki Lima di Kabupaten Jember, terutama pada keberadaan Pedagang Kaki Lima (PKL) di Lingkungan Tegalboto Kabupaten Jember menjadi fokus penelitian. Data dan informasi dalam penelitian terdiri dari hasil wawancara, dokumentasi, arsip-arsip dan buku yang berkaitan dengan fokus penelitian. Teknik analisis data yang dilakukan melalui proses pengumpulan data, reduksi data, penyajian data dan penarikan kesimpulan. Validasi data yang digunakan triangulasi data. Hasil penelitian yang dilakukan oleh penulis bahwa implementasi penertiban Pedagang Kaki Lima sudah berjalan dengan baik meskipun kurang efektif. Faktor penghambat berasal dari internal dan eksternal. Hambatan dari internal adalah kurangnya koordinasi antar organisasi pemerintah daerah, hambatan eksternal muncul dari Pedagang Kaki Lima yang tidak seluruhnya melakukan relokasi. Kata Kunci: Penataan PKL di Lingkungan Tegalboto, Efektivitas kebijakan
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".