The Establishment of Vegetable and Fruit Markets and Nurseries: A Case Study in the Greater Sekhukhune District, Limpopo Province, South Africa
Bibliographic record
Abstract
This paper highlighted the importance of the establishment of vegetable markets, fruit markets and nurseries in the Greater Sekhukhune district, Limpopo Province, South Africa. This entailed taking a representative sample consisting of 69 agricultural projects, with 1096 beneficiaries participating in this research. The following 5 local municipalities were visited: Ephraim Mogale, Greater Tubatse, Elias Motsoaledi, Fetakgomo and Makhuduthamaga. Quantitative and qualitative methods were used in the form of a detailed questionnaire written in English, a focus group discussion, a stakeholder’s discussion, and field observations as part of the data collection. A purposive sampling technique was used to select the 69 projects, in order to cover uniformity and homogenous characteristics such as infrastructure requirements, skills availability, production challenges, agricultural training needs, water source needs, educational level and others. Data was coded, captured, and analyzed with the Software Package for Social Sciences (SPSS version 20) using Descriptive Analysis and Univariate Regression Analysis. The results showed a significant association among the following variables: age, educational level, farming experience, land size, land acquisition, crops planted, water source and market participation. It is recommended that fruit and vegetable markets be established, as well as the creation of a complete, viable agro-value chain that will expand community driven agricultural production and processing.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".