Harnessing Agricultural Potentials for Economic Growth in North Carolina
Bibliographic record
Abstract
Agriculture in North Carolina contributes to 19 percent of the state’s income and employs over 20 percent of the work force. Agricultural activities are significant in rural counties and nearly 30 percent of the total population of North Carolina lives in 85 rural counties. Individuals in these rural counties have less income, education, and employment opportunities eventually in high poverty and unemployment rates. The objective of this study is to examine the potential use of agriculture in economic growth of North Carolina using county level data. Data were gathered from U.S. Bureau of Labor Statistics, U.S. Department of Agriculture, and U.S. Census Bureau for the period of 2000 to 2010. A system of simultaneous equations is used for the analysis. Results highlight that increasing income increases agricultural activities and vise versa. Thus, the counties with high household income levels are more capable of incorporating agriculture in economic growth while the counties with significant agricultural activities are more competent of improving income levels. Overall, results conclude the importance of secured satisfactory level of income through agriculture to enhance economic growth.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".