Do Socioeconomic Factors Matter in Acreage Owned and Acreage Farmed by Small Livestock Producers in Georgia?
Bibliographic record
Abstract
Although socioeconomic factors may influence acreage owned and acreage farmed by small producers, limited studies have been conducted on this topic in the Southeastern U.S., such as in Georgia. Therefore, the study ascertained the effect of socioeconomic factors on acreage owned and acreage farmed by small livestock producers in Georgia. The data were obtained from a sample of producers, and assessed by using descriptive statistics and ordinal logistic regression analysis. The findings revealed that a majority had farming experience and livestock farming experience of 30 years or less, respectively, 82 and 77%. Corresponding proportions for 20 years or less were 74 and 71%. Additionally, a little less than half (48%) owned over 60 acres of land, and a majority (55%) farmed over 60 acres. The ordinal logistic regression analyses revealed that, of the socioeconomic factors, farming status, education, and household income had statistically significant effects on acreage owned and acreage farmed. The findings suggest that socioeconomic factors matter in farm size in the study area, and they should be taken into consideration when designing programs for small producers.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".