Fundamentals of an African-Centred Syllabus in Higher Education in the Post-Colonial Era: The Tehuti Perspective
Bibliographic record
Abstract
Socio-political change in South Africa, also known as Azania,1 brought about the high hopes and opportunities, especially among the black African majority, in this instance, the recognition and revitalisation of their knowledge systems, in particular, in the learning sphere. The name, Azania becomes more relevant in this discussion, as it divulges the basis of indigenous African knowledge and related methodology systems, specifically on issues that encompass knowledge creation, categorisation and classification of events and circumstances. Nevertheless, the review of literature on the significance of aspects of African knowledge creation such as Tehuti perspective is revealed in this instance. This manuscript posits that diverse paradigms often influence the research approach in a particular milieu. It also suggests that the Tehuti perspective will facilitate the eradication of stereotyping imposed by the narrow cultural perspective in the social sphere including education, especially when it comes to research approach. It concludes by highlighting the need for consideration of diverse knowledge value systems, in particular, when dealing with narratives as indicative of a particular milieu rather than a prescriptive.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".