Factors Affecting the Decision of Grant Bank Loans to Economic Institutions â The Case of SMES Tlemcen
Bibliographic record
Abstract
The banking sector is most important sectors that support economic development without them can any economy presses that do its job. Bank credit is effective banking very important, as generated by the return represents the main focus of revenue for any bank no matter how many and varied other sources of revenue; without him, Bank loses his job as financial brokers in economics, but at the same time invest surrounded by risks, especially the financial risks. The aim of this article is to know the factors affecting the decision of grant bank loans to economic institutions. In order to address this problem, we relied on a series of previous studies in this area to study the variables did not study. On all public banks, including foreign and Arab represented in 12 banks. Through direct delivery to form the survey of credit officers and charged with studying the loan files, and to answer a series of questions the variables of the study and the nature of their impact on decision – making in the banks’ employees.
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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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".