An Analysis of the #FeesMustFall Agenda and Its Implications for the Survival of Education in South Africa: The NWU Mafikeng Campus Writing Centre Experience
Bibliographic record
Abstract
The #FeesMustFall-protests were symbolic of unguided social dynamics as stakeholders directly or in directly (indirectly) scramble for escape due to the financial implications that fees increment would engender. South African government is aware of the importance of education in any growing economy as this was demonstrated in the agenda of the post-1994 government in prioritising primary and secondary education, even though the quality of education remained decidedly poor. However, same cannot be said for tertiary Universities in South Africa, the low priority granted to higher education over the past two decades had always been a bone of contention. This paper therefore attempts to interrogate various explanations for fees must fall movement and how this impact on the writing centre at the North-West University, Mafikeng Campus. In contextualizing this problem, the paper employed key elements of Altbach’s empirical theory of student movements. Using Focus Group discussion and by means of Atlas-ti statistical package, the paper demonstrated the richness of data available for analysis and reflects on correlated methodological challenges when attempting to understand student movements and the dynamic relationship between the University environment as well as the country-wide movement, the territorial space and that of writing centre experience during and after the protest. The paper concludes by reflecting and suggesting on elements of a possible research agenda on balancing education and economy.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".