Drivers of Dry Common Beans Trade in Lusaka, Zambia: A Trader’s Perspective
Bibliographic record
Abstract
This study was designed to analyze drivers of dry common beans trade in Lusaka, Zambia. Specifically, the study analyzed the effect of common bean grain characteristics on bean market price. Data was collected using structured questionnaires from 225 traders stationed in three markets namely: Soweto, Chilenje and Mtendere. Using hedonic pricing, the findings reveal that medium sized grain was an important characteristic which significantly affected the pricing of common bean. For instance, it was observed that medium grain size fetched ZMW1.266 per kilogram (kg) and ZMW 1.042 per kg more than grains of smaller size in the pooled and Soweto market sample, respectively. It was further revealed that yellow, yellow and white color significantly affected the bean price received by traders. Other factors which significantly affected the pricing of beans included age of the trader, being a retail trader and trading at Chilenje market. Given these findings, common bean breeders need to include traders and consumers as important actors whose knowledge can make resourceful impact in varietal development. Furthermore, interventions by policy makers that respond to the social economic needs of traders is recommended to improve bean trade.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".