Human trafficking and outcomes for children and young people in the UK
Bibliographic record
Abstract
Human trafficking, ‘modern slavery’ and exploitation have risen up policy agendas as social issues of major global and public concern. In the UK, awareness about the human trafficking of children and young people has grown significantly over the past decade with children making up 44% of all referrals into the UK’s National Referral Mechanism (NRM) in 2023. The views of these children are missing from policy, as is any focus on outcomes. This paper draws on research that scoped international evidence on outcomes and undertook 20 participatory workshops with 31 young people in three locations across England and Scotland. A stark contrast was found between negative outcomes, negative sequalae and negative consequences of human trafficking and the capabilities, strengths and focus on creating positive outcomes when working with young people. Outcomes were ultimately detailed through a Positive Outcomes Framework, anchored in the lives and rights of young people. It is suggested this contrast offers a key insight into a relatively unexplored aspect of human trafficking; that evidence currently misses a focus on positive outcomes in the post-trafficking experience. This risks defining young people solely through their past traumatic experiences, denies their agency and abilities to move forward with their lives.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".