Analisis Ketahanan (Resilience) Pedagang Kaki Lima di Masa Pandemi Covid-19: Studi Kasus Jalan Sukasari, Kota Bogor, Jawa Barat
Bibliographic record
Abstract
Street vendors are one part of SMEs who have experienced the impact of Covid-19, inclusive of street vendors on Jalan Sukasari, Bogor City. The purpose of this study was to determine the most influential factors in recovery and resilience strategies, as well as street vendors in Sukasari street, Bogor City, and alternative recovery strategies for street vendors on Jalan Sukasari, Bogor City. Data collection was conducted by interviews and questionnaire filler by street vendors in Sukasari street. The research analysis used is the Fuzzy-Eckenrode approach analysis as one approach forMulti-Criteria Decision Making. The most influential factors on the resilience of street vendors in Sukasari street, Bogor city after the Covid-19 pandemic based on Fuzzy-Eckenrode analysis are work culture, leadership, and social networks. Alternative strategies based on Fuzzy-TOPSIS analysis are government assistance strategies, digital technology introduction strategies, and debt relief strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".