Sosialisasi Penataan Pedagang Kaki Lima (PKL) Tangguh Pandemi di Pantai Losari Kota Makassar
Bibliographic record
Abstract
Pedagang Kaki Lima (PKL) menjadi salah satu jenis pekerjaan sektor informal yang sangat rentan terdampak pandemi Covid-19. Hal ini juga dirasakan PKL di kawasan anjungan Pantai Losari Makassar. Kondisi PKL di satu sisi banyak bersentuhan dengan lapisan masyarakat menengah ke bawah serta memiliki skala usaha yang relatif kecil. Di sisi lain, kegiatan PKL yang terorganisir dan terencana akan mampu beradaptasi dengan kondisi pandemi serta memberikan dampak yang positif bagi ekonomi lokal kota serta mendukung pemerintah dalam menyediakan lapangan kerja. Kegiatan pengabdian ini bertujuan untuk mengemukakan ide konsep penataan PKL yang tangguh pandemi dan mengedukasi para pedagang tentang penerapan protokol kesehatan di lingkungan kerja PKL. Teknik pengumpulan data yakni observasi, dokumentasi, wawancara, melalui pendekatan stakeholder. Adapun teknik analisis data yakni analisis deskriptif kualitatif. Data yang dikumpulkan digunakan dalam penyusunan konsep penataan PKL yang tangguh pandemi, sharing, dan transfer knowledge kepada para PKL dalam bentuk Focus Grup Discussion (FGD) dilengkapi pemutaran video perencanaan konsep penataan dan edukasi penerapan protokol kesehatan. Hasil kegiatan pengabdian berupa konsep penataan PKL meliputi penataan lapak, meja dan kursi, perbaikan jaringan air bersih, penyediaan fasilitas cuci tangan, tempat sampah, kontainer sampah, dan skema pembuangan sampah, serta sosialisasi pentingnya penerapan protokol kesehatan bagi pedagang dan pengunjung.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".