Emerging perspectives on 'African development' : speaking differently
Bibliographic record
Abstract
Contents: George J. Sefa Dei/Paul Banahene Adjei: Emerging Perspectives on 'African Development': An Introduction - George J. Sefa Dei: Democracy, Good Governance, and Education: Rethinking African Possibilities - Paul Banahene Adjei: Toward an Ontological and Epistemological Understanding of Indigenous African Process of Conflicts and Disputes Mediations and Settlements (CADMAS) - Aman Sium: Dreaming Beyond the State: Centering Indigenous Governance as a Framework for African Development - Laura H. V. Wright: Transforming Canada's Hegemonic Global Education Paradigm through an Anticolonial Framework - Jadie McDonnell: Challenging the Euro-Western Epistemological Dominance of Development through African Cosmovision - Susan Otto: Centering African Indigenous Women within the Context of Social-Economic and Political Development - Francis Akena Adyanga: Regional Integration, a Prospect for Development: Lessons from Rwanda's Experience in the East African Community - Ahmed Ali Ilmi: Rethinking Development:An Indigenous African Communal Approach - Michael Onyedika Nwalutu: Basic Education and Sustainable Development in the Era of Globalization: Does Nigeria Mortgage Her Future in Accepting International Assistance for Her Primary Education Projects? - Isaac Nortey Darko: Environmental Stewardship and Indigenous Education in Africa: Looking Beyond Eurocentric Dominated Curricula.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".