Healer-driven ethnoveterinary knowledge diffusion among semi-nomadic pastoralists in Karamoja, Uganda
Bibliographic record
Abstract
Karamojong, semi-nomadic pastoralists of Uganda, rely on indigenous knowledge (IK) for their own healthcare and their livestock’s. It is important to preserve, promote and protect IK, in order to keep it from disappearing. One way is to facilitate its diffusion. The aim of this study was to compare the status of ethnoveterinary knowledge (EVK) in three unrelated communities to investigate whether organised healer-promoted EVK is more easily diffused and to what extent. This study applies a ‘knowledge, attitude and practices’ (KAP) survey to measure EVK application relating to twelve livestock diseases and sixteen remedies in different communities. Only in the community of Nabilatuk do registered healers regularly meet for participatory EVK sharing and afterwards pass on ideas to neighbours. Participants from the Lorengedwat community rarely interact with Nabilatuk while the interviewees of the Kaabong group have had virtually no chance to interact with the two other communities. In total 180 people (60 per site) were interviewed. Data were analysed in relation to distance from the healers’ association; this significantly influenced EVK scores. Overall Nabilatuk scores were higher than those obtained in both other villages, while Lorengedwat was higher than the most distant and remote community of Kaabong. This indicates that organised healers have been effective in divulging their information and in promoting EVK diffusion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".