African Indigenous knowledge versus Western science in the Mbeere Mission of Kenya
Bibliographic record
Abstract
This article sets out to explore the way in which Western science and technology was received in the Mbeere Mission of central Kenya since August 1912 when a medical missionary, Dr T.W.W. Crawford, visited the area. In his dalliance with ecclesiastical matters, Crawford, a highly trained Canadian medical doctor, was sent by the Church Missionary Society (CMS) at Kigari-Embu, in 1910, to pioneer the Anglican mission in the vast area that included Mbeereland, where Mbeere Mission is situated. Contending with the African indigenous knowledge in medicine, environmental conservation, agriculture and other forms of indigenous science, the introduction of Western science and technology, 1912 to 1952, the article argues, did not erase the former; rather, it complimented it. Pockets of general resistance were evident, though Mbeereland, unlike its neighbouring Mutira Mission of 1912, did not offer elaborate opposition to the Western science and technology, partly because the locals could have learnt about it from their neighbours who had experienced it much earlier. Through a historico-narrative design, the research article endeavours to primarily review the coming of Western medicine in Mbeereland: Did it conflict with the African medicine? Methodologically, the data have been collected via archival sources, oral interviews and by reviewing applicable literature.Contribution: The input of this research article to the HTS Journal's vision and scope is seen by appreciating its focus on the interface between African indigenous knowledge and the European science and technology. Although the main focus is
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".