Mapping the New Generation of Academics Programme (nGAP) to the C.O.S.T.A. Postgraduate Research Supervision and Coaching Model: A Value Proposition for New Researchers in South Africa
Bibliographic record
Abstract
The Department of Higher Education and Training's (the Department) New Generation of Academics Programme (nGAP) is a prestigious program that involves the recruitment of highly capable scholars as new academics. These academics are recruited based on carefully designed and balanced equity considerations, as well as the most critical disciplinary areas in the higher education system. The nGAP is currently the largest program within the Staffing South Africa's Universities Framework (SSAUF), a component of the University Capacity Development Programme that focuses on university staff development (UCDP). The C.O.S.T.A. Postgraduate Research Supervision and Coaching Model is a tool that has been developed to capacitate both researchers and supervisors with a specific intent to lighten the journey of research for postgraduate students in South Africa. For students who have never been exposed to research methods, postgraduate research is a huge obstacle. Students' lack of exposure to research language was one of the challenges revealed in a recent study of postgraduate supervision. Whereas difficulties students face with methods within the positivist and realist philosophical dimensions are not insurmountable, a variety of approaches, including rigor determination in qualitative research results in complexities, which present a plethora of challenges to novice researchers. This document introduces the C.O.S.T.A. model as a tool for academics and students, with a systematic guide to understanding foundational concepts and the language of research, as well as making informed decisions about research methods and design strategy options available to the prospective researcher. Furthermore, the tool is also powerful instrument for monitoring and measuring development and performance of researchers. With five nodes that fundamentally inform application (Concepts, Objective, Situation, Tact and Assessment), the C.O.S.T.A provides a framework and a standard against which researchers should benchmark their development.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".