‘Black like Me’: A Critical Analysis of Arrest Practices Based on Skin Color in the Gauteng Province, South Africa
Bibliographic record
Abstract
Objective: This article looks at the everyday life and realities of current practices employed by the South African Police Service (SAPS) officials, by shedding light on the experiences and practices on profiling search and effecting arrest based on race and skin color in the Gauteng Province. Particularly, this article examines the experiences of the SAPS officials to measure police perception of the skin color of foreign nationals, and to establish if wrongful arrests were linked to skin color stereotyping. Methods: The theoretical approach employed the social identity theory (SIT) was used to interpret the results. A survey questionnaire consisting of the New Immigration Survey (NIS) Skin Color Scale with 10 shades of skin color mapped to a pictorial guide, as well as a self-report measure on wrongful arrests, was administered to 80 SAPS officials, who performed visible policing duties. The research sample consisted of two SAPS groups from two different contexts, namely township and urban contexts. The Statistical Package for the Social Sciences (SPSS) software was used to conduct Pearson’s correlation and comparative analyses. Results: The results showed that the SAPS officials stereotyped foreign nationals as dark-skinned. The skin color stereotype was, however, not correlated to wrongful arrests. The study concluded that although respondents perceived that South Africans were distinguishable from foreign nationals based on skin color or tone, identification processes were not influenced by this stereotype belief.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".