Bibliographic record
Abstract
Contents Introduction Academic Freedom in Africa: Between Local Powers and International Donors Hocine Khelfaoui & Ibrahim Oanda Ogachi ......................... v Libertés académique en Afrique : entre pouvoirs locaux et bailleurs de fonds internationaux Hocine Khelfaoui & Ibrahim Oanda Ogachi ............................. xv The Challenges of Feminism: Gender, Ethics and Responsible Academic Freedom in African Universities Amina Mama ...................................... 1 Neo-liberalism and the Subversion of Academic Freedom from Within: Money, Corporate Cultures and ‘Captured’ Intellectuals in African Public Universities Ibrahim Oanda Ogachi ................................ 25 Academic Freedom and the Constriction of Democratic Space in Botswana: Reflections from an Academic-cum-Politicianin the University Space Taolo Lucas ............................................. 49 Regulatory Bodies, Academic Freedom and Institutional Autonomy in Africa: Issues and Challenges – The Nigerian Example Elijah Adewale Taiwo ............................. 63 Peer Review Mechanisms: The Bottleneck of Academic Freedom Elizabeth Ayalew ......................................... 91 Liberté académique en question : contribution à la problématisation d’une notion à partir du cas estudiantin au Sénégal Abdoulaye Guèye ............................ 113 Libertés académiques, syndicalisme et politique en Côte d’Ivoire Goin Bi Zamblé Théodore .................................. 133 Pursuing Excellence in a ‘World-Class African University’: The Mamdani Affair and the Politics of Global Higher Education Isaac A. Kamola .................................... 147
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.499 | 0.245 |
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".