Microcredit in Cambodia: Why is There So Much Support for a Failed Poverty Reduction Model? (preprint)
Bibliographic record
Abstract
Cambodia’s microcredit sector – the world’s largest (in per capita terms) and most profitable – has created a raft of negative economic and social phenomena that are increasingly undermining the functioning of the economy and cohesiveness of society. Three especially damaging trajectories associated with Cambodia’s microcredit sector are: (1) a ‘no-growth’ low productivity economic structure built on an ‘extractivist’ logic; (2) reckless lending strategies that have created dangerous levels of over-indebtedness in the poorest communities; and (3) the widespread use of land titles as collateral which has inevitably led to the growing loss of land by the poor through coerced informal sales. The COVID-19 pandemic may require the government to bail-out struggling microcredit institutions in Cambodia. A logical, pro-poor way forward would be to make such bail-outs conditional upon an agreed conversion of existing privately/foreign owned microcredit institutions into local community-owned and controlled financial ones. Debt-for-equity swaps would be one way to achieve this goal.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".