Bentrovato, D. & Wassermann, J. 2020. Teaching African History in schools: Experiences and perspectives from Africa and beyond [Book review]
Bibliographic record
Abstract
This book provides a remarkable collection of contributions that raise and discuss serious \nissues associated with teaching African history in schools. All the case studies show an \nexceptional sensitivity to the dangers and opportunities associated with teaching and \nlearning African history across the continent and beyond. Cases are drawn from South \nAfrica, Kenya, Rwanda, Zimbabwe, Malawi, Cameroon and Tanzania, as well as the \nteaching of South African history outside Africa in the United Kingdom and Canada. \nThis is relevant in raising Afrocentric voices and contributions to existing debates in the \nglobal field of history education. The book provides an in-depth examination and analysis \nof nine individual and comparative empirical studies. It highlights thematic issues related \nto the history curricula and textbooks with content knowledge, pedagogical content \nknowledge and activities on how African history is diffused in schools. The book presents \nthoughts and dialogical conversations of teachers and learners on history curriculum \nimplementation coupled with pedagogical practices on African history focusing on \nprimary schools, secondary schools and preservice teacher education at the tertiary \nlevel. Additionally, consideration is given to the challenges and opportunities of tackling \nsensitive and controversial issues in the history classroom such as engaging with national \nhistories of trauma, racial or ethnic discrimination and intercommunal wars and conflicts. \nThe proceeding sections, present a chapter-by-chapter summary highlighting a few details \naligned with the main argument of the book.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".