African Leadership in the Diaspora: Diffusion, Infusion, Synergy, and Challenges
Bibliographic record
Abstract
The concept of leadership has a long history but gained vogue in Africa with the emergence of democracy and end of colonialism. Leadership, however, cannot be understood independent of context and so there have been questions of what African leadership is, African leadership in the diaspora, African leadership styles, and the future of Africa. The combination of past linkages, traditions, culture, history, and indigenous habits creates unique leadership styles that are distinctly African. Traditional leadership ontologies must acknowledge how leadership has evolved in ways distinct to the African experience. Collective and practiced ontologies of leadership must attend to the ways dialogic exchange, relationship, and socio‐material meaning take on a unique character when viewed through the lens of African culture and context. For Africans living outside of the continent (the diaspora), the expression and practice of leadership is embroiled with many issues. Studies on African leadership identify some features of African leadership culture and how those features play out on the identity, style, and development of African leaders exploring leadership as a vehicle for development in Africa. Using systematic review of the literature, the paper explores African leadership in the diaspora through dominant collective and practice leadership ontologies and cultural hybridity.
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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 | 0.035 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".