Beyond conventional models: Lending by Native Community Development Financial Institutions
Bibliographic record
Abstract
Abstract Native Community Development Financial Institutions (Native CDFIs) have become an increasingly important source of credit and financial services in the areas on or near American Indian reservations in the United States. Guided by a conceptual framework developed on the basis of the related finance literature and drawing on loan‐level data from eleven Native CDFI loan funds, we offer the first systematic quantitative analysis of lending in the Native CDFI industry. As hypothesized, Native CDFIs on average give out small loans but support borrowers in varied circumstances with diverse loan products. Important predictors of delinquency include both conventional, hard information‐based, measures of client risk, and alternative, soft information‐based, community‐informed and character‐based measures. Overall, these findings lend strong support to holistic approaches for assessing client creditworthiness for Native CDFI operations. More generally, our analysis contributes new insights into the operations of an industry that plays an instrumental role in removing barriers to socioeconomic development in Native communities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".