Crossing borders: Does it matter? Differences between (near-)domestic and cross-border sex traffickers, their victims and modus operandi
Bibliographic record
Abstract
Hardly any research exists that empirically compares (near-)domestic and cross-border sex trafficking. The few studies that do are based on relatively small samples and only represent US data. This study substantially extends the scarce scientific knowledge about the differences between the two types of sex trafficking, based on European data. Our sample consists of all 658 (near-)domestic sex traffickers and all 424 cross-border sex traffickers, registered by the prosecution service in 2008–2017 who are brought to court in the Netherlands. We collected data on these traffickers from registers of the prosecution service, from a file analyses on the indictments/verdicts, and from registers of Statistics Netherlands. These data provide insight into the characteristics of the traffickers, their victims and modus operandi. Our findings show that significant differences between the two types of sex trafficking exist, which is of great importance for better tailored prevention and identification strategies. The most prominent finding is that the threshold to get involved in (near-)domestic sex trafficking is lower than for cross-border sex trafficking. (Near-)domestic sex traffickers are, compared to cross-border sex traffickers, younger (as are their victims), they seldom need to migrate, they operate on a smaller scale (more one-to-one and for a shorter period of time) and practically never in a criminal organization. Furthermore, they use violent means of coercion to control their victims more frequently than cross-border sex traffickers, which can be interpreted as additional evidence for a less organized practice. These findings contribute to a more complete understanding of sex trafficking, in particular of the traffickers who were seldom the direct subject of research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".