Human trafficking and human rights violations in South Africa: Stakeholders' perceptions and the critical role of legislation
Bibliographic record
Abstract
This article examines the perspectives of governmental and nongovernmental stakeholders in South Africa on the dynamics of human trafficking in South Africa, and on efforts to protect the human rights of rescued victims of human trafficking prior to the promulgation of human trafficking legislation in the country. The authors seek to understand the range of views and approaches of stakeholders to trafficking, including possible links to HIV, as human trafficking is commonly discussed in the media, but empirical research on the scale, dynamics, and impacts of trafficking in South Africa is scarce. This exploratory situation analysis involves desk review and 24 key informant interviews, using purposive and sequential referral sampling. Respondents included government departments and non-governmental organisations working at a border-crossing site (Musina), and two major destination sites for irregular migrants, including trafficked people (Johannesburg and Cape Town).\nAlmost all respondents reported that human trafficking is significant and complex, and that both cross-border and internal movement of trafficked victims violate victims' rights in several ways. While they suffer at the hands of organised crime syndicates, their rights are further violated even after rescue, prior to the recently-promulgated human trafficking legislation in the country. Victims' access to justice is also either delayed or denied in many cases due to the inability to prosecute the perpetrators. The study concludes that, despite the recent giant step in the right direction in promulgating human trafficking legislation in South Africa, there is a need for further efforts by the South African government to take additional proactive and practical measures for optimum effectiveness of the law without which the goal of the Act may remain a tall dream.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".