Exploring the Experiences of Female Graduate Students in African Universities: Questions about Voice, Power, and Responsibility
Bibliographic record
Abstract
Consistent economic downturns, political uprisings, and social upheavals from the 1980s have significantly depleted the quality of higher education in Africa, particularly graduate training. While remarkable strides in graduate training have been made in countries such as South Africa relative to other parts of the continent, policy and funding challenges continue to threaten the quality of students and programs. Over the past two decades, new forms of institutional collaborations aimed at revamping graduate training in sub-Saharan Africa have emerged. Debates on how to revamp the higher education system are ongoing among scholars, policymakers, administrators, and funders, but minimal attention is paid to the students’ voices, particularly women’s that speak to the dire conditions under which graduate training is carried out. To spur more discussion about this gap in literature, we conducted focus group discussions with female graduate students in four higher education institutions in Nigeria and South Africa. Our participants identified five major challenges that graduate students often wrestle with: financial challenges, limited sources of and dated curricular materials, institutional infrastructure and program logistics, academic supervision, and gender relations among students as well as between students and scholars. These challenges, our participants assert, often place female graduate students in a more vulnerable position than their male counterparts. Our findings, though preliminary, point to the need to actively engage students, especially women, in academic debates and initiatives aimed at improving graduate training in Africa.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".