Centering Ukama Ethic (Relatedness) in the Covid-19 Pandemic ‘New Normal’ in African Higher Education
Bibliographic record
Abstract
This conceptual article examines the ukama ethic concerning the Covid-19 pandemic-induced ‘new normal’ in African higher education. In so doing, we endeavor to appropriate ukama which is a communally oriented value system to militate against socially isolated individualism in Remote Learning and an Ethic of Care that combats social prejudices occasioned by the Covid-19 pandemic in African higher education. Our central argument is that Ukama ethic is contextually appropriate in the Covid-19 pandemic-induced ‘new normal in African higher education. This article does two important things. Firstly, in light of the demands for local thought traditions in African higher education, it advances the social values of relatedness that constitute Ukama ethic to normatively underline the Remote Learning and Ethics of Care. In this regard, a question that is important to us is; if not now in the ‘new normal, then when can local thought traditions be fully incorporated into African higher education? Secondly, in the attempt to appropriate local thought traditions into African higher education, the article offers a critical reflection of Ukama. Despite its limitations, we conclude that the Ukama ethic is important in the Covid-19 pandemic induced ‘new normal’ in African higher education.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".