Comparing the operations and challenges of pig butchers in rural and peri-urban settings of western Kenya
Bibliographic record
Abstract
The purpose of this cross-sectional, observational study was to describe the pig butcher enterprises in western Kenya; highlighting differences in the operational processes and challenges between rural and peri-urban settings. Fifty pig butchers were interviewed using questionnaires in two districts, Kakamega (peri-urban) and Busia (rural). Results showed that pig butchers were central to the coordination of activities required to connect pig farmers to pork consumers in their communities. Several differences between rural and peri-urban enterprises included use of agents to find pigs, average market weight of pigs, pig prices per kilogram, transport and marketing. Butchers were challenged by credit and capital constraints, seasonality, high pig prices and high search costs. Butchers should be encouraged to have pork inspected and should be included in outreach programs intended to prevent the spread of zoonotic pathogens since they are the last intervention point before pork is consumed. Use of the tape measure for estimating pig weight could help remove inequalities between farmers and butchers abilities to estimate pig weights and could help to reduce search costs for the butcher, thus increasing equity and efficiency of trade between farmers and pig butchers in western Kenya. Key words: Africa, Kenya, pig butchers, marketing channel, smallholder.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".