UMKM PARTICIPATION IN HALAL INDUSTRY: THE LEGAL FRAMEWORK AND OPPORTUNITIES
Bibliographic record
Abstract
Economic growth is an indicator used to see development success in a country, including Indonesia. The Covid-19 pandemic has a significant impact on economic growth, such as a decline in each quarter of 2020. It also affects the income and level of consumption of the community. UMKM, as the spearhead of economic affairs, can certainly provide solutions to the problems faced. The tendency of the halal industry, especially in halal products, is currently experiencing a panic, where all products must have a halal certificate. This study aims to analyze the potential of halal-based UMKM on the economy after the Covid-19 pandemic. The research method is qualitative with a literacy approach, referring to the findings that correlate with the studied variables. The result of this research is that halal-based UMKM provides economic improvement. The population of Muslims in Indonesia reached 229.62 million people, and it has an impact on the level of consumption of foods and drinks labelled as halal, especially during a pandemic. The tendency that occurs determines consumption in society. Therefore, halal-based UMKM have the potential to improve the economy during the pandemic and post Covid-19 pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".