Mentoring for Social Change: Challenges and Future Directions for PhD Mentoring in South Africa
Bibliographic record
Abstract
In this paper, I reflect on my experiences of mentoring within PhD programmes over the past 18 years. Drawing on autoethnographic methods, I aim to critically reflect on the challenges and opportunities of mentorship programmes in the South African context. I identify 5 areas of debate that are central to better understanding mentorship programmes. These are: the management of mentorship programmes, the mainstreaming of mentorship within PhD programmes, the relationship between mentorship and supervision, the relationship between mentorship and transformation and the risks inherent to mentorship programmes. Based on these reflections I recommend a research agenda that can lead to more clearly conceptualised mentorship programmes and can better evaluate their impact both on PhD candidates and the broader transformation of the academy.
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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 | 0.040 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".