East African Higher Education and the limitations of institutional reforms: A case study of selected public universities
Bibliographic record
Abstract
Globally, universities are engaged in various aspects of reforms to improve their outlook and relevance. In East Africa, despite the similarities in many dimensions of socio-economic conditions, universities vary in terms of focus and extent of engagement in educational reforms. In order to examine this phenomenon more closely, three purposely-selected East African public universities were studied. Analysis of related documents as complemented by responses from key officials of these institutions revealed several findings: the University of Dar es Salaam’s reforms seemed to conform more to characteristics of competitiveness-driven reforms, the University of Nairobi exhibits equity-driven reform, and Makerere University practises finance-driven reforms. Furthermore, the findings register limitations of effective institutional reforms such as massification of higher education, infringement of university autonomy, emerging technologies, paradox of internationalization, and the incapacity to cater to holistic students’ welfare. The study concluded that, despite the myriad of limitations that the universities face, they have numerous opportunities which if efficiently utilized will enable them run the higher education race more triumphantly. The paper recommends that strategies for reforms should not derail the universities from their mandate to serve their respective countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".