Do Small and Mid-scale Beginning Farmers and Experienced Farmers Feel the Same About Farm Financial and Profitability Position?
Bibliographic record
Abstract
This study examined various levels of feelings that small and mid-scale farmers in Kentucky have towards financial and profitability situations of their farm operations. The study uses mailed and online survey data collected from 129 small and mid-scale farmers in 2017. We used an ordered Probit model to analyze data. Findings indicate that the probabilities for small and mid-scale farmers to feel positively, fairly, and negatively are 36 percent, 55 percent, and 9 percent, respectively. We found that small and mid-scale beginning farmers are significantly less likely to feel positively than experienced farmers. Findings showed that those who are knowledgeable about agricultural marketing and agricultural economics are more likely to feel positive. These findings are useful for policymakers, outreach specialists, and other agencies seeking to improve the financial and profitability position of small and mid-size farms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".