ANALYSIS OF THE SOCIO-ECONOMIC EFFECT OF MICROCREDIT ON MICRO-ENTREPRENEURS USING THE SELF-REPORTED PERCEPTION METHOD AND RELATIONSHIPS WITH OTHERS
Why this work is in the frame
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Bibliographic record
Abstract
Microcredit offers an innovative response to non-traditional financing and development needs for marginalized individuals. Here, impact assessment is very useful in that it helps to determine whether or not the objectives set at the onset are achieved and what can be done to correct the impediments to achieve better results. The paper analyzes the socio-economic effect of microcredit through the novel dual approach of self-reported perception and relationships with others. The data were gathered in collaboration with the Fonds Mauricie in November, 2019. Apart from the improvement in the financial indicators of micro-enterprises, the results show that microcredit has enhanced micro-entrepreneurs’ living conditions and family situation at rates of 88 and 91 percent, respectively. Regarding morale, 88 percent of micro-entrepreneurs report feeling better and optimistic about the future, and 92 percent report better relationships with others. In particular, the socio-economic effect of microcredit is determined by a better family situation, better living conditions and better financial situation and business income. These results imply that microfinance institutions must extend their financing to all segments of the population, especially the most vulnerable people such as immigrants and indigenous peoples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it