SUSTAINABLE COLLABORATION IN HIGHER EDUCATION: THE CASE OF THE CAPE PENINSULA UNIVERSITY OF TECHNOLOGY (SOUTH AFRICA) AND FONTYS UNIVERSITY (NETHERLANDS)
Bibliographic record
Abstract
This paper is centred around the partnership between the Cape Peninsula University of Technology (CPUT) and Fontys University of Applied Sciences. Research indicates that a crucial dimension of international orientation is the internationalisation of education. Internationalisation is an instrument to improve the quality of education or research, it helps to prepare students for a professional career, which is increasingly international as a result of the global knowledge economy and teaches competencies with a wide application range (Beck, 1998). An increasingly internationalised economy, will require larger and larger pools of welltrained, multilingual, internally knowledgeable employees. (Excerpt, Policy on Internationalisation at a Canadian university) International higher education has also been affected because of the globalisation of our societies and economies. Increasing competition for international students and international programmes is one of the consequences. The changing economic position of countries like India and China in the world is also a factor. For the cooperation between CPUT and Fontys we are working for a sustainable collaboration with certain basic principles in mind. The questions “why” and “what” are used as a guide in the partnership instead of just looking for additional exchanges, more mobility programmes. The focus is rather on quality and not quantity. We are offering a best practice in collaboration on different academic levels between higher education in Europe and Africa, especially the Netherlands and South Africa.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".