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Record W3130312686 · doi:10.1080/23322373.2021.1878807

Business management education in the African context of (post-)Covid-19: Applying a proximity framework

2021· article· en· W3130312686 on OpenAlexaff
Sherwat Elwan Ibrahim, Alan Fowler, Moses Ν. Kiggundu

Bibliographic record

VenueAfrica Journal of Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsContext (archaeology)ScholarshipCoronavirus disease 2019 (COVID-19)SociologyCrisis managementPsychological resiliencePublic relationsPolitical scienceEngineering ethicsEconomic growthPsychologyEconomicsEngineeringSocial psychologyGeographyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.248
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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