Data Envelopment Analysis (DEA) Approach for the Jordanian Banking Sector's Performance
Bibliographic record
Abstract
<p class="zhengwen">This study sought to evaluate the performance of banks in the Jordanian banking sector, where DEA approach has been used for a sample of banks operating in Jordan amounted to 16 banks (10 Jordanian banks and 6 foreign banks operating in Jordan) during 2014 and by using the variables: Deposits and liabilities<strong>,</strong> Total expenses<strong> </strong>and Dedicated asset as main inputs for banks and which represent the main activity of banks, and the variables : Credit facilities<strong> </strong>and Net Income<strong> </strong>as outputs of the banks using the statistical software SIAD.</p>The current study has concluded that all banks operating in Jordan have a surplus in resources untapped optimally and over the investment opportunities available to these banks, and the reason beyond this may be due to the reservation policy of banks, especially after the mortgage crisis suffered by these banks. The study has also concluded that foreign banks operating in Jordan have achieved efficiency ratio more than the Jordanian banks, and this can be attributed to the financing experience of foreign banks’ managements and their international spread which is more than the Jordanian banks’.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".