Techne meets Metis: Knowledge and practices for tick control in Laikipia County, Kenya
Bibliographic record
Abstract
Prevention of tick borne diseases is often through tick control practices. This article diagnoses tick control practices, knowledge underlying these practices and how knowledge is shared at the wildlife-livestock interface in Laikipia County, Kenya. It identifies diverse land use and tick control practices by different land and livestock owners from a scientific knowledge-based (techne) and context driven experiential knowledge-based (metis) perspective. Interviews, focus group discussions, observations and documents yielded qualitative data to unravel i) the historical development of tick control in Kenya ii) techne and metis tick control practices within three ranches and among pastoralists in Laikipia County, and iii) status of tick knowledge sharing between stakeholders. Historical tick control measures date back to about 100 years ago, with increasingly strong veterinary measures over the decades under government control. However, the veterinary control system collapsed around 1991 and livestock owners took tick control into their own hands. All respondents indicated having relevant techne available about tick ecology and tick management practices. To adapt to the changing social, economic and institutional context, they further developed metis, integrating the known techne. Metis and techne complemented each other. Our study reveals that metis is developed within stakeholder groups. The data also suggest that metis practices sometimes develop risky effects to animal, human and environmental health. Knowledge on tick control is mainly shared within a social group, not between groups. We esteem, knowledge sharing between different stakeholder groups (ranchers, pastoralists, DVS) may provide opportunities for better informed decision-making based on fruitful combinations of techne and metis for effective and safe tick management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".