Punitive Welfare on the Margins of the State: Narratives of Punishment and (In)Justice in Masiphumelele
Bibliographic record
Abstract
While there is an established literature on the relationship between political economy and state punishment, there is less work on how punishment is constituted from below in contexts of inequality. This article analyses the discourse around incidents of lethal collective violence that occurred in 2015 in a former black township in South Africa. I use this as a lens for examining how punitive forms of popular justice interact with state punishment. Whether via the slow violence of structural inequality or the viscerally corporeal high rates of interpersonal violence, my interviewees were intimately acquainted with violence. Although they supported long-term imprisonment, none of them came across as stereotypical right-wing populists. Instead, they adopted complex positions, calling for a type of punitive welfarism, which combined harsh solutions to crime with explicit recognition of the importance of dealing with ‘root causes’. I argue that when the state is perceived to be failing to both impose punishment and provide welfare, violence becomes a technology of exchange, which simultaneously seeks both more punishment and more welfare. The result is an assemblage of exclusionary penal forms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.027 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".