Indigenous Knowledge and Vocational Education: Marginalisation of Traditional Medicinal Treatments in Rwandan TVET Animal Health Courses
Bibliographic record
Abstract
This study explores Rwandan ethno-veterinary knowledge and the degree to which this knowledge is reflected in the country’s technical and vocational education and training (TVET) instruction. The knowledge considered is the Indigenous medicinal knowledge used by rural Rwandan livestock farmers to treat their cattle. Through interviews with farmers, TVET graduates and TVET teachers, and an examination of the current TVET Animal Health curriculum, the research identifies a neglect of Indigenous knowledge in the curriculum, despite the fact that local farmers use numerous Indigenous medicinal innovations to treat their animals. The focus of the Rwanda’s TVET Animal Health curriculum is on Western-origin modern veterinary practices. The authors argue that this leaves Rwandan TVET Animal Health graduates unprepared for optimal engagement with rural farmers and with the full range of potential treatments.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".