When Torture Mocks the Law: Understanding Police Brutality in South Africa
Bibliographic record
Abstract
South Africa promulgated the Prevention and Combating of Torture of Persons Act No. 13 of 2013, which criminalises the use of torture by law enforcers. The Act also criminalises cruel, inhumane, or degrading treatment or punishment of citizens by law enforcers. However, the implementation of this law is derisory as the torture and physical abuse of civilians by the police reportedly continue unabated. This phenomenon seems part of police culture that is entrenched in South African policing practices. Prior to the study, the literature review underscored the unabated prevalence of police violence. Against this background, this article seeks to highlight specific incidences of police officers’ use of unconstitutional and abusive acts of torture involving civilians. Using a qualitative research approach, ten officers of the Independent Police Investigative Directorate (IPID) were interviewed to generate the required data. Thematic analysis was used and the findings revealed that civilians suspected of criminal behaviour were often exposed to inhumane forms of torture, which ranged from food and water deprivation to being strangled, suffocated, and electrocuted. These forms of torture involving suspects were reportedly prompted by the urgency for eliciting information, ‘proving’ the presumption of guilt, proactively preventing crime in communities, and coercing suspect compliance. The findings thus urge the need for a blanket ban on the torture of suspects, the effective investigation by the IPID of cases of torture, and the successful trial and conviction of police perpetrators of this crime.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| 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".