Towards a Scholarship of Practice for University Leadership in Southern Africa: The Two-Way Practitioner-Researcher Loop
Bibliographic record
Abstract
Vice chancellors of public universities in the Southern African Development Community (SADC) region face a myriad of challenges that require research- and data-driven decision-making. This paper presents a decision-making model for college and university leadership - The Two-Way Practitioner-Researcher Loop. This scholarship of practice has the twin goals of developing a knowledge base for college and university leadership and improving leadership practice in the university. The scholarship of practice comprises two “loops”. In the practitioner-to-researcher loop, vice chancellors develop practitioner-defined research agenda to be researched internally by Departments of Institutional Research and externally by members of Higher Education research communities. In the researcher-to-practitioner loop, research findings are communicated back to vice chancellors for immediate application to institutional planning, policy formulation, and decision making. This scholarship of practice develops a knowledge base comprised of both “knowledge for practice” and “knowledge in practice” at the level of university leadership. To build capacity for vice chancellors to craft research agenda and questions emanating from their “knowledge in practice”, we identify internal mechanisms and external associations, training programmes and other forums that provide leadership development and support for these university executives.
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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 | 0.138 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.074 |
| Scholarly communication | 0.038 | 0.034 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.011 | 0.010 |
| 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".