Championing Inclusivity: Underrepresentation of Women in African Academic Leadership and Scholarly Journal Management
Bibliographic record
Abstract
Objectives – This study seeks to investigate the exclusion of women from the management of scholarly journals across East Swahili (Kenya, Uganda, Tanzania, South Sudan, Ethiopia, and Anglo-West Africa (Ghana, Nigeria, Sierra Leone, Liberia, Gambia) by delving into the implications of marginalization. Furthermore, the study aims to illuminate the often-overlooked experiences of black women, whose narratives are frequently overshadowed by those of black men or subsumed within the context of white women. Methods – By employing empirical evidence from African Journals Online (AJO) and institutional journal data from countries in focus, this study examines the pervasive domination of men within scholarly journal management in East Swahili and Anglo-West Africa. Results – Findings reveal a widespread dominance of men in the management of scholarly journals in the targeted countries despite the considerable presence of women in academia. Conclusion – The underrepresentation of women in academic leadership positions carries significant consequences, including a lack of diversity in decision-making processes. Such homogeneity can perpetuate existing disparities and impede progress towards gender equality within academia. Furthermore, discussions concerning gender inequality in academia often neglect the experiences of black women.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".