Measuring the Impact of the Corona pandemic on bank credit in the Kingdom of Bahrain
Bibliographic record
Abstract
The Coronavirus (COVID-19) pandemic is a disease caused by the new Coronavirus, whose symptoms began to appear in China in December 2019. The World Health Organization has classified COVID-19 as a severe pandemic, and some cases of the disease have caused deaths. The new coronavirus can spread from person to person. For this reason, countries closed their airports, stores, and places of economic importance, which led to the fall of some countries from the economic point of view due to the pandemic and the complete closure of the country. The current research examines the impact of the Corona pandemic on the performance of bank credit in the Kingdom of Bahrain in a sample of banks in the Kingdom of Bahrain, which are three conventional banks and three Islamic banks. The research was based on the financial reports for the third quarter of the year 2020. A person from the selected banks in order to reach the desired results. The method used in the research is a descriptive and analytical approach. The research found results, the most important of which is that some banks in the Kingdom of Bahrain suffered a severe loss by comparing their financial reports in September of 2019 before the Corona pandemic with September of 2020 in the Corona pandemic. The questionnaire demonstrated a decrease in bank credit to banks by customers from a month ago, March to September of 2020.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".