Professors without Borders: The Benefits of a Scholastic Mentoring Program
Bibliographic record
Abstract
Universities in sub-Saharan Africa currently struggle to maintain adequate faculty and resources to take on Ph.D. candidates. Expanding enrolment in recent decades has not been met with improvements in university facilities, and neglect from development agencies has made it difficult for the higher education sector to meet the demands of the knowledge economy. As a result African graduate students have few opportunities to pursue postgraduate study in the region and sub-Saharan Africa’s brain drain persists. In order to address the lack of opportunity for graduate study, the Professors without Borders program has been developed. Professors without Borders is a mentorship program, whereby graduate students in sub-Saharan Africa are partnered with professors and academics at universities in industrialized nations and the students are mentored during the course of their degree. The program aims to promote internationalization among universities as well as facilitate development. This report examines the motivation behind the program and its potential for success. The literature review on higher education in sub-Saharan African summarizes the problems facing the sector but indicates the potential for higher education to contribute to economic growth. In addition, the reception of the Professors without Borders idea among African universities indicates unanimously that such a mentorship program would be very much welcomed and beneficial to African Ph.D. students. The experience of a similar program known as BrainRetain by the Irish-Africa Partnership provides insight into the challenges and logistics of making such a mentorship program successful and sustainable.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".