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 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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".