Statistical Analysis of Small Holder Farmer Financial Exclusion: Case Study of Migori County, Kenya
Bibliographic record
Abstract
There are estimated to be approximately 600 million small scale farmers globally, and they produce most of the food consumed, especially in the developing countries. The farmers, however, are often unable to obtain optimal crop yields due to their exclusion from the financial systems in their countries, which deem them too high risk to lend to. This results in the farmers being unable to afford optimal inputs into their farms, hence depressing their yields and the level of food security. This study aimed to statistically determine whether the small scale farmers of Migori County in Kenya are financially excluded or not, and to what extent. Data were collected from the farmers through a questionnaire survey, and subsequent statistical analysis has shown that indeed the small scale farmers of Migori are financially excluded to a large extent. Consideration of non-financial data in the farmers’ credit rating has been recommended as a way forward towards their financial inclusivity. This study provides scientific proof of smallholder farmer financial exclusion, which proof is generally difficult to find, especially in the developing countries.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".