Do Socioeconomic Factors Matter in Acreage Owned and Acreage Farmed by Small Livestock Producers in Alabama?
Bibliographic record
Abstract
Socioeconomic factors could affect acreage owned and acreage farmed by small producers. However, there is limited research on the issue in the Southeastern U.S., for example, Alabama. Thus, this study examined the impact of socioeconomic factors on acreage owned and acreage farmed by small livestock producers in Alabama. The data were collected from a convenience sample of producers from several counties in Alabama, and analyzed using descriptive statistics and ordinal logistic regression analysis. The results showed that a majority had farming experience of more than 30 years, but had livestock farming experience of less than 30 years. Also, a little over half owned over 60 acres of land, and a majority (58%) farmed over 60 acres. The ordinal logistic regression analyses showed that, of the socioeconomic factors, only age and education had statistically significant effects on acreage owned and acreage farmed. The findings suggest that socioeconomic factors, specifically, age and education, are important to farm size in the study area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".