THE INFLUENCE OF BPUM ON THE REGIONAL ECONOMY DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The research objective is to analyze the influence of BPUM on the Regional Economy by considering HDI, poverty levels, PAD and labor force in MSMEs. This type of quantitative research uses panel data, namely secondary archival data on the population of Micro, Small and Medium Enterprises in cities and districts in all provinces in Indonesia. Data was obtained from the Ministry of Cooperatives, Small and Medium Enterprises, the Directorate General of Fiscal Balance (DJPK) and the Central Statistics Agency (BPS) for the period 2020 and 2021. The research population for all districts in Indonesia in 2020 and 2021 was 514 districts and cities. Based on the results of the Fixed Effect Model, the influence of BPUM on GRDP obtained a coefficient value of 0,010803, the influence of HDI on GRDP obtained a coefficient value of -0,0006866, the influence of poverty levels on GRDP obtained a coefficient value of 0,0212617, the influence of PAD on GRDP obtained a coefficient value amounting to 0,0009767, and the influence of the labor force on GRDP obtained a coefficient value of -0,0331359.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".