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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".