Bibliographic record
Abstract
With millions of people trapped in modern-day slavery, human trafficking is largely misunderstood, owing to limited data and research. Present-day human trafficking trends are linked to issues such as corruption, funding, public awareness, and poor anti-trafficking coordination. Over centuries, human trafficking has taken on a variety of forms ranging from enslavement in all its forms to organ removal. South Africa’s most significant achievement in combatting human trafficking is its own anti-trafficking legislation, namely the Prevention and Combatting of Trafficking in Persons Act of 2013. However, some provisions of this Act remain ineffective, thus impeding the fight against trafficking in the country. This article focuses on South Africa’s trafficking trends and anti-trafficking responses. It also highlights the hindrances obstructing the effective enforcement of its legislation by comparison to the first-world country Canada, to gain an understanding of effective anti-trafficking administration and execution to ultimately provide recommendations for South Africa to follow. For example, years before South Africa, Canada had already responded to international pressures regarding its anti-trafficking efforts. The country focused ample resources and funding on its anti-trafficking task team while South Africa followed a piecemeal approach in addressing human trafficking. This stems from a misunderstanding of the crime and policy frameworks, and mismanagement of funds. This article proposes that the South African government should strengthen its anti-trafficking measures by making funds easily accessible to victims and educating front-line responders to communicate effectively with victims.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".