The Networks of Smallholder Organic Horticultural Farmer Organizations and Other Value Chain Actors for Their Change in Two Selected Regions in Tanzania
Bibliographic record
Abstract
With the agricultural reforms of 2000s in Sub-Saharan African countries including Tanzania that aimed to capacitate farmers in various areas including technological and marketing areas, the purpose of the study was to determine how smallholder organic horticultural farmer organizations under non-governmental organizations are contemporarily networking with other organic horticultural value chain actors for their change. The study was undertaken in Morogoro and Kilimanjaro regions of Tanzania. The study employed mixed design informed by social network analysis approach. Most smallholder organic horticultural farmer organizations under local umbrella non-governmental organizations are networking with limited capacity in disseminating, spreading and bridging technological and marketing knowledge and information, inputs and organic horticultural products to other value chain actors. These networks are concentrated on some value chain nodes and vice versa. Consequently, the weak networking has reduced their capacity to benefit from value chain in various ways including failure to cut their transaction costs, increase their bargaining power and economies of scales. This implies that more has to be done to connect remote smallholder organic horticultural farmers with numerous services. That broadly covers the services they scantly receive for those they are totally inaccessible to them. The findings call for various actors to find different strategies to increase the applicability of functional physical and virtual distances between them. This manuscript contributes to the applicability of social network theory in smallholder organic horticultural farmer organizations under non-governmental organizations in Tanzanian context for betterment of organic horticultural sector.
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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 | 0.000 | 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 | 0.000 |
| 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".