Social Capital as a Determinant of Microfinance Clients' Outcomes
Bibliographic record
Abstract
In this paper, I examine the role of social capital as a determinant of microfinance clients’ ability to leverage microloans to improve key socioeconomic metrics. Social capital theories are deployed in an attempt to account for differentials in these metrics among a unique sample of borrowers. Microfinance emerges prominently in the literature on poverty reduction. While it is important to remain critical of policy agendas that emphasize solutions such as microfinance, it is also necessary to understand how disadvantaged groups can effectively negotiate the system. A deeper understanding of how and whether social capital plays a role in microfinance borrowers’ success may allow us to assess what types of supports will increase the likelihood of success for clients in these programs. Based on survey data collected for this thesis, I was unable to demonstrate a trend between social capital and changes in socioeconomic metrics, but did discover other interesting trends.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".