Memorials and shifting meanings of rural revolts in South Africa: the Mpondo rural revolts and insurgent scholarship
Bibliographic record
Abstract
This article uses the case of the commemoration of the Mpondo Revolts and the massacre by the state of defenceless villagers in Mpondoland in 1960 to argue that the political elite engage in distortions of history for political gain. The ruling party elite have over time omitted or added narratives about the revolts, thus gradually marginalising their significance. Part of the distortion of the history of the revolts is the gradual attempt to change the conversation during the annual commemoration event of the revolts and the Ngquza Hill massacre. While local people continue to be disenfranchised from their land, ironically now by the post-apartheid government, politicians at the memorial event focus not on the issues that were the causes of the revolts, especially the struggles around land, but on apparent local needs, such as electrification, access to clean water and bringing revenue to the villages through tourism. However, memorialisation of historical events is prone to these contested histories and narratives because of the political and financial support of the government in power, institutionalising both tangible and intangible aspects of the history that is being memorialised. It is only through defiant or insurgent scholarship that more accurate versions of the history of events such as the Mpondo Revolts can help to maintain their significance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".