Understanding Policing of Human Trafficking in Gauteng Province, South Africa: The Phenomena, Challenges and Effective Responses
Bibliographic record
Abstract
Human trafficking is one of the most heinous crimes perceived to be a serious and growing problem worldwide. Human trafficking is a depressing phenomenon that affects many people across the globe. This study attempts to determine the phenomena of human trafficking, and identify the existing challenges of policing this scourge and suggest possible effective responses. This study was carried out utilising a qualitative approach. Forty interviews were carried out among officials deployed in the Directorate for Priority Crime Investigation (DPCI), the South African Police Service (SAPS), the Department of Home Affairs (DHA), the Department of Social Development (DSD), the Gauteng Provincial Office, as well as with the victims regarding their views and experiences on the stakeholder’s involvement in combating and investigating human trafficking. The key findings indicated that the challenges are corruption, lack of motivation and commitment to combat human trafficking, lack of limited awareness and information about the human trafficking scourge in South Africa, the findings also indicated a lack of clear strategy and response by stakeholders to successfully investigate, prosecute and incarcerate the perpetrators of human trafficking and the findings further indicated lack of capacity, resources and training to deal with human trafficking. Based on the findings, the author provided, possible recommendations such as; the utilisation of advanced technology and use of intelligence-led policing to strengthen the work of stakeholders, advanced training and better education including improved awareness strategies; and the utilisation of social media as a tool to deal with human trafficking and strengthening of enforcement responses and reporting techniques.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".