“Why can't you pay if you can eat?”: Tales of How Women Encounter Unpleasant NGO Practices in Bangladesh
Bibliographic record
Abstract
Abstract In Bangladesh and globally, microcredit has been recognized as a key development tool in the alleviation of poverty. Many international development agencies and donor countries prioritize microcredit to alleviate poverty because of reported success stories of microcredit nongovernmental organizations (NGOs) in mobilizing poor women to participate in incomegenerating activities. Microcredit NGOs construct success stories of alleviating poverty and gender equity in relation to the repayment rate, but little is known about how they deploy strategies to collect loan installments from borrowers. Using ethnographic data collected in the Chittagong Hill Tracts (CHT) of Bangladesh, I examine how microcredit NGOs create unequal power relations between fieldworkers and borrowers to facilitate secure loan recovery. Reflecting on the women's experiences with microcredit programs, I demonstrate how these microcredit NGOs impose the provision of group liabilities, a ‘forced choice,’ upon the borrowers and how they socialize the borrowers into a culture of shaming to enforce repayment obligations. Instead of contributing to the development of norms of cooperation and solidarity that socially and economically empower the entire community, I argue that NGOs instead empower a group of female borrowers, serving their capitalistic interests, which often stimulates social conflict and negatively affects social solidarity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".