Expandindo o insight fundamental dos grupos de empréstimo de micro finança: em direção a um novo framework multinível
Bibliographic record
Abstract
Os grupos de empréstimos (GEs) foram criados a partir do insight de que seu arranjo possui a capacidade de reduzir os problemas de assimetria de informação e de enforcement, os quais excluem os tomadores de crédito mais pobres dos bancos tradicionais. Neste artigo, objetivamos expandir este insight ao propor que o sucesso dos GEs não depende apenas do seu arranjo estrutural, mas também da configuração positiva de seus níveis contextuais macro (ambiente institucional), meso (a rede social dos seus tomadores de crédito) e micro (os quadros cognitivos dos tomadores de crédito e dos trabalhadores- chaves da IMF). Nós apresentamos sete efeitos que conectam todos os três níveis em uma primeira sugestão de um framework multinível. Por fim, apresentamos um exemplo real que demonstra o funcionamento deste framework.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".