Cars, compounds and containers: Judicial and extrajudicial infrastructures of punishment in the ‘old’ and ‘new’ South Africa
Bibliographic record
Abstract
This paper examines non-state infrastructures of vigilante violence in marginalized spaces in South Africa. I argue that car trunks, shacks, containers, and other everyday receptacles function as the underside of official institutions, such as prisons and police lock-ups, and bear historical imprints of the extrajudicial punishments inflicted on black bodies during colonialism and apartheid. I focus on two techniques: forcing someone into the trunk of a vehicle and driving them around to locate stolen property, and confinement in garages, shacks, containers, or local public spaces. Whereas in formerly 'whites only' areas, residents have access to insurance, guards, gated communities, fortified fences, and well-resourced neighbourhood watches, in former black townships and informal settlements, this is not the case. Here, the boot, the shack, the shed, the car, and the minibus taxi play multiple roles, including as vectors and spaces of confinement, torture, and execution. Thus, spatiotemporality affects both how penal forms permeate space and time, and how space and time constitute penal forms. These vigilante kidnappings and forcible confinements are not mere instances of gratuitous violence. Instead, they mimic, distort, and amplify the violence that underpins the state's unrealized monopoly over the violence inherent in its claims to police and punish.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".