Donors’ Support for Microcredit as Social Enterprise: A Critical Reappraisal
Bibliographic record
Abstract
Over the last decade or so, we have observed a proliferation of microfinance institutions (MFIs) in developing and transitional economies as well as in some developed countries such as USA and Canada.Modelled on infamously ‘successful’ institutions such as the Grameen Bank of Bangladesh, the Badan Kredit Kecamatan (BKK) of Indonesia or BancoSol of Bolivia, new strands of ‘innovative’ microfinance institutions have been established specifically for reaching ‘the poor’, in the field of ‘micro-enterprise finance’ and ‘poverty lending’. Drawing lessons from the widely acknowledged failure of the earlier experiments with credit interventions such as the targeted rural credit programmes based on subsidized interest rates, new microcredit programmes lean heavily towards ‘market-based solutions’, with use of concepts such as group lending and joint liability contracts. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".