Business management education in the African context of (post-)Covid-19: Applying a proximity framework
Bibliographic record
Abstract
What happens when Covid-19 meets Africa? To find answers, this article examines tertiary management education delivered by the continent’s business schools in the context of Africa’s susceptibilities to the pandemic. The concept of proximity is applied as an axiomatic analytic complement to Covid’s transmission pathways impacting on the psychosocial foundation of human relations, people’s spatial distribution and their time perspectives. Taking management literature into account, proximity is applied to Africa’s business schools in terms of their immediate and long-term responses to the pandemic, suggesting practical post-Covid reforms considered from a humanistic management approach to management education and scholarship. A theme throughout this article is that Covid-19’s exposure of contextual vulnerabilities presents an opportunity and imperative for business schools’ re-missioning and renewal to enhance relevance, quality and building post-Covid resilience. The article provides a framework for the study of other Covid-sensitive sectors or organizations and theory development and testing using different proximity conceptualizations, frames and combinations thereof. Limitations of the study are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".