Bibliographic record
Abstract
Contents Continuity and Change in Students’ Account of Race and Class Relations at a South African University Deevia Bhana .....................................1 Funding Higher Education in Africa: State, Trends and Perspectives Damtew Teferra ........................................19 Towards Evaluating a Higher Education Residence Environment that is Conducive to Learning, Development and Success Willem Petrus Wahl ..................................53 Reengineering Postgraduate Curricula and its Mode of Delivery: Graduate Students’ Views of Modular Course Delivery at Addis Ababa University Solomon Areaya, Ayalew Shibeshi & Daniel Tefera ....................71 Massive Open Online Courses (MOOCs) and Green Economy Transition: Feasibility Assessment for African Higher Education Godwell Nhamo ................................103 The History of Makerere Institute of Social Research (MISR) and Her Place in the Study of the Social Sciences in Africa Stanley Baluku Bakahinga Mbalibulha.................................123 The British Model, ‘Africanization’ of the Curriculum and other Issues: The Influence of Professor D. W. Ewer (1913-2009) on University Teaching in Ghana and on Biological Education in Africa Richard Alan Baker ...................................... 145 “...University is ISO 9000:2008 Certified”: Neoliberal Echoes, Knowledge Production and Quality Assurance in Kenyan State Universities Ishmael I. Munene .........................................163
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.532 | 0.283 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".