Agri-Preneurial Resilience and Success: The Correlation and Demographic Characteristics of Smallholders in South Africa
Bibliographic record
Abstract
The incentives and subsidies needed to stimulate growth, resilience, and success in agri-preneurial businesses will only be realized through numerous interventions as agri-preneurship contributes significantly to sustainable agricultural development in South Africa. This study provided a novel insight into agri-preneurial resilience and success and evidenced the hypothesis that there is no significant positive correlation between agri-preneurial resilience, farm success, and selected demographic characteristics of smallholders. We surveyed a total of 200 agri-preneurs who were willing and able to participate in this study. This study used a structured questionnaire that was divided into the following sections: (i) demographic information; (ii) agri-preneurial resilience; and (iii) agri-preneurial success. Descriptive statistics and a regression analysis were employed to articulate responses. Four distinct models were employed to ascertain the goodness of fitness and the hypothesis, and assess the relationship between success, resilience, and selected demographic characteristics of agri-preneurs. To determine resilience, the Connor–Davidson Resilience Scale (CD-RISC) with 10 items was used because the CD-RISC justifies the best psychometric characteristics that portrays the levels of resilience amongst agri-preneurs. In measuring success, the scale items were graduated and ranked on a 5-point scale from 1 to 5. The reliability of the scale was also tested, and α = 0.93 was obtained. This study obtained a Cronbach alpha value of 0.96, indicating optimum reliability. Additionally, we ran a factor analysis to certify the reliability of the variable, which gave one factor from the four items. Significant positive correlations were found between gender, age, education, income, household size, diversification, and agri-preneurial resilience and success. This study concluded that most of the selected demographic characteristics were predictors of agri-preneurial resilience and success. However, demographic variables may be influenced by numerous factors given the heterogeneity of agri-preneurs in the study area.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".