Transforming Doctorate Education: Views from Supervisors
Bibliographic record
Abstract
This paper will discuss how a transnational, structured, dual-award doctoral programme, jointly delivered by two South African universities and one British university, is focusing on promoting policy and strategy shifts, curriculum innovation, and course development as part of transformation imperatives at the heart of academic enterprise in advancing inclusive educational futures. The doctoral partnership, which is funded by the Department of Higher Education and Training, aims to make progress towards two of WSU’s strategic objectives, which are to increase the percentage of academic staff with doctorate qualifications and to increase research capacity and output. These objectives were recently emphasised by the VC and Principal, in her encouragement for staff to pursue postgraduate opportunities, to forge forward in teaching, research, and community engagement, enabling WSU to be an impactful African university. Adopting a collaborative autoethnographic approach, we explore how our doctorate programme initiative has been experienced by supervisors from the three institutions as they negotiated differing time zones, IT infrastructure and access, institutional and national policies, and supervision cultures. Findings of the study will inform recommendations for theory, practice, policy and research.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".