Disentangling to Fortify The Crimes of Slavery, the Slave Trade and Human Trafficking
Bibliographic record
Abstract
This article offers a brief background to the crimes of human trafficking, slavery and the slave trade which often take place in and as a result of conflict situations, such as in Ukraine. These crimes are, however, often conflated and misunderstood: not only by the general public, but also by professionals working in this field. Even though these crimes are commonly conflated, each crime has specific requirements or elements, and address egregious conduct.Their legal histories and policy base are distinct yet complementary. Their dual juridical availability could provide meaningful, effective redress for women and men, both adult and child survivors. The article intends to contribute to untying the knots, to disentangle the misconceptions of human trafficking from the slave trade and slavery.The endeavour is not theoretical, but practical. It is done to fortify the pursuit of human trafficking as a transnational crime and the slave trade and slavery as international crimes. In short, this article aims to explore, distinguish, and harmonise the available jurisdictions for human trafficking, slavery and the slave trade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 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".