The Response of South Africa’s Policy Landscape to Global and Local Trends - The Case of a Rural-Based University
Bibliographic record
Abstract
Globalisation is a highly contested term and has become a site for tension. Whilst globalisation in the context of Higher Education must be lauded for giving students and scholars access to work on a global platform, it has also reinforced inequalities that are already prevalent and has even created new ones. The negative impact of globalisation is evident most explicitly in developing countries such as South Africa and in smaller institutions of Higher Learning such as the University of Zululand. To compound the already precarious situation, one finds that in addition to accommodating elements of globalisation, institutions of Higher Learning have to also accommodate local trends such as the Africanisation of Education. Whilst globalisation has swept across the world and has impacted almost every sphere of life, the foci of this paper is on how policies in Higher Education, with special reference to the University of Zululand, have responded to global trends and local needs. This is a qualitative study. The methodology used is document analysis. This study interrogates the Language and Research policies of the University of Zululand to illuminate on how the aforementioned institution responds to global and local needs. The main finding of the study is that institutions of Higher Learning, in crafting their policy documents, are incorporating globalisation and are finding innovative ways of embracing local trends at the same time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".