Bibliographic record
Abstract
In Lebanon, microfinance is not specially developed. Financial institutions that allocate microcredits are NGOs that are mildly supported by the government. The activity of these institutions affects only 11.5% of the population (IFC, 2008, cited by Mayoukou et al., 2013, p.4). These authors note the lack of empirical data related to microcredit granted by microfinance institutions in Lebanon, particularly regarding the characteristics of their beneficiaries. Our study emphasizes the characteristics of beneficiaries of microcredit allocated by MFIs (microfinance institutions) in Lebanon. As a result of data obtained from MFI heads, the results seem to show that NGOs MFIs give more credit to men than to women, and a low percentage of credit goes to startups. In addition, beneficiaries have a low level of education, poor or moderately poor, and are located in rural areas. Gender discrimination in the allocation of micro-credits was highlighted on the basis of the first data processed in this work. The results of the interviews with MFI’s administrative officials seem to show that the men loan officers may distinguish between male and female beneficiaries and prefer to grant microcredit to a man. Women beneficiaries may have less information about the credits offered by them, or do not take initiative because they live in a patriarchal society. Moreover, men go through their wives to get another microcredit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".