Economy Impact of the COVID-19 Prevention Policy on Business Continuity and Welfare of Street Vendors
Bibliographic record
Abstract
There is quite a lot of research on COVID-19, but research on the impact of COVID-19 prevention policies on business continuity and the welfare of street vendors has not been widely studied. This study examines the economic impact of COVID-19 prevention policies on business continuity and the welfare of street vendors. The regression value or the effect of the COVID-19 prevention policy on business continuity is 0.918. The coefficient of determination is 0.842, which means that the impact on business continuity is 84.2%. The regression value of the COVID-19 prevention policy on the welfare of street vendors is 0.934, with a coefficient of determination of 0.873. This means that the impact of the COVID-19 prevention policy on the welfare of street vendors is 87.3%. This study has limitations in one location in Semarang, and the research subjects are mostly culinary street vendors. The direction of future research is the impact of policies related to the pandemic or national economic crisis and the global crisis on the business continuity of street vendors and other informal economy business actors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".