Group Cohesion for Enhanced Operation of Agricultural Community-Based Projects in Gauteng Province, South Africa
Bibliographic record
Abstract
Collective operation of smallholder farmers and cooperatives has been attributed to many mishaps and malfunctions. Such knowledge creates misperceptions regarding agricultural cooperatives and their usefulness in development. This study investigated member commitment, group cohesion and membership retention in agricultural production cooperatives. The main aim was to identify possible practical measures for enhanced performance and increased sustainability in farmer organizations. Data was collected from 92 participants that were currently operating as cooperative members. A combination of descriptive statistics, Perceived Cohesion (PC) and Binary Logistic Regression methods were employed for analysis. Results of the study indicate that group cohesion is influenced by trust among members, internal communication, financial performance of the cooperative, involvement of members in decision making, and role of the organization in the community. Strategies for increased group cohesion that were recommended in the study include information sharing and transparency at all levels of operation, and collective decision making and planning in organizations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".