Victimisation of African Foreign Nationals in Durban, South Africa
Bibliographic record
Abstract
Media reports of the continued violence and discrimination experienced by African Foreign Nationals1 in South Africa have brought into the forefront victimisation of this sector, despite pledges by the government and law enforcement agencies to put a stop to it. This is also linked to current social milieu debates taking place within international trends on migration. Studies and evidence have shown that although the attitudes towards foreign nationals vary across South Africa's socio-economic and ethnic spectrum, foreigners who live and work in South Africa do face discrimination by citizens, some government officials, members of the police, and by private organisations who are contracted to manage their detention and deportation. This research, with a qualitative approach, explores the persistent issues that threaten African Foreign Nationals. Fifty participants were selected through a purposive sampling technique. The main aim of this research was to examine the issues that threatened the safety and security of African Foreign Nationals in Durban, South Africa. It was found that offences such as physical assault (i.e. Grievous Bodily Harm - GBH), arson, rape, verbal abuse, house robberies, property damage as well as discrimination were serious crimes perpetrated against African Foreign Nationals which was often characterised by xenophobic violent attacks against them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".