Agriculture Credit in Developing Economies: A Review of Relevant Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>This paper aims to present a comprehensive review of 110 studies on agriculture credit in developing countries during 1995 to 2015. The literature has been classified and presented on the basis of time period, country of study, methodology used, issues covered, and sources of study. Agriculture credit has gained interest of policy makers and researchers in developing economies in recent years with raising concerns of issues like food security and rising population. However, the situation of small and marginal farmers is still vulnerable and they lack timely and adequate access to institutional sources of finance. Non-institutional sources of credit are still dominant in rural credit markets; while the role of micro-finance appears dubious. This study will prove helpful for policy makers and future researchers who wish to study diverse issues in rural finance in general and agriculture credit in particular.</p>
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it