Using Multicriteria Decision Analysis to Assess Stakeholders Motivation toward Sustainable Packaging in the Fruit and Vegetable Value Chains of Rural Uganda
Bibliographic record
Abstract
The use of better protective packaging is often viewed as a low-cost alternative for short-run reduction of post-harvest losses in the fruit and vegetable supply chains of sub-Saharan Africa. We present the exploratory results of mapping the fruit and vegetable value chain actors in rural Uganda and analyzing their influence on the adoption of packaging. We propose a decision-aiding model based on multicriteria analysis to obtain a motivation score for assessing the degree of interest of the value chain actors toward packaging. Because it combines quantitative and qualitative information, our approach constitutes a valuable tool in the context of planning for an appropriate implementation of an appropriate packaging technology. The results suggest that distributors are the most favorable for adoption of packaging, followed by collectors, farmers, and customers, in that order. By demonstrating the use of a multicriteria decision approach for identifying the actors most likely to adopt packaging, this work suggests that the proposed approach could be used by potential regulators and manufacturers to focus their efforts on developing a well-targeted packaging deployment strategy.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".