Impact of Government Policy on BPR Health in EastJava during the COVID-19 Pandemic
Bibliographic record
Abstract
This research examines the implementation of government policies in improving the national economy during the Covid-19 pandemic that hit Indonesia. This research is quantitative research using data from quarterly financial reports from 2020 to 2021 at BPRs in East Java. The sample used in this research was 812 data. The results obtained from this research include that in general the health level of East Java Province BPRs in the 2020 to 2021 period using the RGEC (Risk Profile, Good Corporate Governance, Earnings, Capital ) method is getting better. This is based on health level results such as ROA and CAR, the results of which received the best composite rating, namely composite rating 1 (very good), while the results of the NIM ratio calculation have increased every quarter. In calculating the health level of the NPL ratio from the first quarter of 2020 to the fourth quarter of 2021, it is ranked composite 4 (high risk). For the application of GCG in this research, we cannot include the calculation results due to limited data. This is because very few BPRs publish the results of GCG implementation assessments on their websites.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".