Kinship networks and financial inclusion nexus: the mediating effect of extended social cohesion among poor microentrepreneurs post COVID-19
Bibliographic record
Abstract
Financial inclusion has been a major tool for poverty alleviation in Africa and, as such, it has become a topic of great interest among scholars working on the difficulties of African microentrepreneurs to access financial resources. Thus, this research examines whether extended social cohesion mediates the relationship between kinship networks and financial inclusion of poor microentrepreneurs post COVID-19 in the unbanked rural sub-Saharan Africa. Analyzing data from a sample of 304 microentrepreneurs of Uganda, we find that extended social cohesion resulting from social capital significantly affect the relationship between kinship networks and financial inclusion as it acts as substitute for the lack of physical collateral. The theoretical contribution of this research is that, it introduces the social cohesion theory, a theory developed in sociology, in the literature of microcredit finance and shows that social cohesion can serve as collateral, of which the absence has often prevented microentrepreneurs from having access to microcredit.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".