Influence of Transaction Costs and Governance in the Marketing of Organic Pineapples from Uganda
Bibliographic record
Abstract
The organic pineapple sub-sector in Uganda has existed for slightly over 10 years. Whereas the sub-sector targets the organic market, slightly more than half of the organic pineapples produced by farmers are sold in this market and the rest is sold to the conventional market. This study aimed at determining the transaction costs that limit the amount of organic pineapples sold by farmers to the organic market. The study also aimed at establishing the relationship between the transaction costs and governance of the transactions between farmers and exporters. Data were collected from 140 organic pineapple farmers and seven organic pineapple export companies. Qualitative methods and econometric methods were used in data analysis. Findings show that there were high asset specificity and uncertainty in organic transactions, which resulted into farmers selling only a proportion of their produce to exporters. Involving farmer in contract formulation, trust, distance to collection centers and high asset specificity increased the proportion of pineapple sold by the farmers while farmers’ experience reduced the proportion sold. There were three forms of governances between farmers and organic exporters; the captive, modular and relational governance. The relational governance had the highest transaction costs, and less proportion of organic pineapples were sold in this governance. The study recommends transaction cost reduction strategies such as organizing farmers in cooperatives, trust building and engaging farmers in contract formulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".