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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".