ANALYSIS OF THE SOCIO-ECONOMIC EFFECT OF MICROCREDIT ON MICRO-ENTREPRENEURS USING THE SELF-REPORTED PERCEPTION METHOD AND RELATIONSHIPS WITH OTHERS
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".