Multi-Institutional Partnerships for Higher Education in Africa: A Case Study of Assumptions of International Academic Collaboration
Bibliographic record
Abstract
Public and private universities in Kenya, Tanzania, Uganda, South Africa, and elsewhere in Africa, were experiencing all time high enrollments since the late 1990s. To address these demands, university administrators sought partnerships with universities of the global North to facilitate the necessary educational reform and curriculum transformation to meet the needs of the increased enrollments. In spite of these efforts, in the past 10 years the partnerships failed to meet expectations. A case approach was used to study reports, journals, interview notes, surveys, and qualitative data collected during 2007 – 2012 from one university selected purposely to shed light on partnerships and linkages with African universities. The authors examined the expectations, dynamics, and intricacies of academic partnerships and the reality of African academic institutions. The analysis revealed perplexing assumptions that undergird the expectations of collaboration between U.S. and African partners as well as cross-cultural dynamics that govern, sustain, and sometimes frustrate such engagements.
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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".