Examining Farm Financial Management: How Do Small US Farms Meet Their Agricultural Expenses?
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
Small farms in the US have significant challenges in financial management. This study examines how small farmers undertake farm financial management to meet their agricultural and farm-related spending and expenses. Using primary survey data from Tennessee, the study investigates the factors influencing the extent of use of five financing sources to meet the spending and expenses: cash/fund directly generated from the sale of agricultural products, farmer’s past savings, farm household’s off-farm income, income/incentives from government payments, and external loans. Using negative binomial regression estimation of generalized linear models, findings suggest that the decision on the use of financing sources is significantly influenced in general by age, education, income and land acreage holdings, off-farm work, and risk factors related to farmer or farm household. However, the associated factors and their effects on the extent of use are different depending on the financing source.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it