POLITICS AND VIOLENCE IN THE ‘RUSSIAN ZONE’: CONFLICT IN NEWCLARE SOUTH, 1950–7
Bibliographic record
Abstract
The urban townships of South Africa have been contested terrain since their inception. Different groups have struggled to control territory, various resources and political activities within the confines of the locations and, all too frequently, violence has been an integral part of these struggles. Groups as varied in composition and ideology as squatter movements, well-organized criminal outfits, student groups, vigilantes, traditional courts (makgotlas), migrant gangs, youth gangs, municipal political groups and national political movements – with much overlapping between these categories – have all at one time or another sought to impose their will on township residents and have regarded violence as an essential element in their campaigns. While much attention has been deservedly devoted to the violence employed by the state as a means of subjugating, dividing and controlling township residents, the different ways in which black urban groups struggled to assert control over their environments have received relatively little scrutiny. These processes cannot be regarded in isolation from the state's quest for control, but neither should they be subsumed by the larger focus on a revolutionary struggle. Rather, I would argue that a more informed understanding of the conditions and challenges faced by black urbanites requires study of the nature of localized power and violence within the townships. African groups pursued agendas which served their own interests and had a considerable impact on social relations and perceptions of power and authority, both within the locations and in the broader context of national/racial politics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".