Leadership Diversity in Africa and The African Diaspora
Bibliographic record
Abstract
The motivation for this chapter is to demonstrate how leadership is diverse in Africa and the African diaspora. The chapter summarizes the findings from the LEAD (Leadership Effectiveness in Africa and the Diaspora) research project. The chapter incorporates the findings of the LEAD research in Kenya, Uganda, Tanzania, Ghana, Egypt, the Caribbean, the United States, and Canada. This is made possible by an analysis of the results from Delphi Technique and focus groups discussion used in the LEAD project. Using these techniques, we have been able to develop a greater understanding of leadership diversity in Africa and the African diaspora. African countries are among the most ethnically, religiously, and culturally diverse in the world with many ethnic groups split into multiple states that are different in terms of language, culture, and ethnic composition. The study found that some aspects of leadership diversity are not included in the popular Western measures and concepts as found in the extant literature. For example is the case of the big men, Ubuntu, religion and spirituality. It is important to recognize that the LEAD results took place in selected African countries, but more countries have been included for future research. The chapter provides a broader understanding of how leadership is diverse in Africa and the African diaspora.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".