The Dynamic of Red Plate Bank's Financial Performance Before and During the Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted business activities, including the banking industry. The Indonesian State Bank (BNI) might face the same challenges as other banks. However, as a well-established red plate bank, the dynamic of this bank needs to be further investigated to show how the pandemic affects its financial performance. The main objective of this study is to provide empirical evidence on whether the COVID-19 pandemic affects the financial performance of the red plate bank. In doing so, the current researchers conducted a comparative analysis using paired sample t-tests. Time series data based on BNI's quarterly financial reports from the first quarter of 2017 to the fourth quarter of 2022 were collected through documentation. The data were partitioned into two groups, which were data before the pandemic and during the pandemic. The paired sample t-test results show that, in general, there is no statistical difference in BNI's financial performance before and during the pandemic. The finding confirmed that, as a red plate bank, BNI is a well-established bank with a sound financial state regardless of the 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".