‘Rainbow is not the new black’: #FeesMustFall and the demythication of South Africa’s liberation narrative
Bibliographic record
Abstract
In the 1990s, South Africa transitioned from apartheid to liberal democracy. Heroes, place names, holidays and symbols were revisited and replaced to reflect a ‘new’ nation and delineate a clear break from the ‘bad old days’. Central to this nation-building narrative is the figure of Nelson Mandela as a unifying hero exemplifying the ideals of this new nation. South Africa is now experiencing another transition. The so-called ‘born free’ generation mobilised for the first time on a mass scale in the 2015–2016 #FeesMustFall (#FMF) student protests at universities nationwide. Initially focussed on financial accessibility of higher education, these massive protests also questioned the rhetoric, narrative and heroes of the ‘new’ nation, reprising counter-hegemonic and hidden scripts to deconstruct the post-1994 hegemonic discourse and expose enduring inequality. Centring our analysis on interviews with Pretoria-based protesters, we position the students as experts on themselves distilling theoretical insights that emerge from their articulated experiences. We show that students engaged in a powerful project of dismantling a national narrative, questioning Nelson Mandela as ‘father’ of the nation, rejecting the unifying and temporal terminology that rhetorically placed apartheid’s inequalities in the past, and calling for the deconstituting of South Africa, the settler-created polity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".