Elements of Opportunity and Poultry Farms Performance in Delta State, Nigeria
Bibliographic record
Abstract
The paper examines the existence, if any, of differences in gross margin between rural and urban areas in Delta State, Nigeria. Data were collected from all 275 poultry farmers registered with the Delta State Ministry of Agriculture, Livestock Department. The null hypotheses was that there is no significant difference in poultry farm gross margin between locations in terms rural and urban areas; managers with formal education in agriculture and managers who have no formal education in agriculture; and managers who have and who do not have prior experience in poultry business. Data were collected from all 275 poultry farmers registered with the State Ministry Agriculture using copies of a structured questionnaire and were analyzed using frequency counts, means and T-test. Amongst the findings were: Majority of poultry business operators have low level formal education in disciplines not related to agriculture; there was a significant difference in the mean number of years of schooling and courses studied between rural and urban areas but that there was no significant difference in number of years of prior experience. The T-test results failed to reject the three null hypotheses. The study concluded that indeed elements of opportunity may vary from place to place but the ability to exploit the benefits may moderate or accentuate performance. Entrepreneurial capacity building was recommended for poultry business operators’ state wide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".