“Like shooting fish in a barrel:” recruitment into sex trafficking in emergency shelters for youth experiencing homelessness
Bibliographic record
Abstract
• Youth experiencing homelessness often possess a ‘perfect storm’ of risk factors for being trafficked. • Homelessness is more than a risk factor for being trafficked. Traffickers actively target youth who are homeless in shelters and proximate areas. • Targeting factors among youth include isolation and loneliness, a need for safety, a lack of basic needs and homelessness. • The physical space, number of residents, level of training and awareness among staff, and policies and procedures of shelters can increase risk. • Service providers can mitigate risk by mindfully designing and monitoring spaces, working from a trauma-informed lens, and training staff. Youth experiencing homelessness often possess a ‘perfect storm’ of risk factors which traffickers exploit to lure and recruit them into sex trafficking. Despite this, little research has investigated how to prevent youth experiencing homelessness from being trafficked, including in the spaces that are meant to provide respite to them, such as emergency shelters. This paper utilizes findings from 23 semi-structured interviews with survivors of sex trafficking to investigate how and why the experience of homelessness among young people increases their vulnerability to being trafficked and how service providers can reduce the risk of this happening. The results show that traffickers actively target youth experiencing homelessness in shelters by posing as residents, sending others inside to pose as residents and loitering in the areas outside shelters to find potential victims. Qualitative analysis yielded two central themes that, when combined, make shelters attractive and effective spaces for traffickers: individual-level targeting factors among young people and elements of the emergency shelter system. The individual-level factors identified were isolation and loneliness, the need for safety, a lack of basic needs and the experience of homelessness. The emergency shelter factors included large physical spaces with numerous residents, a lack of staff training and awareness, the staff’s demeanour and approach, and various policies and procedures that have unintentional consequences. Mindfully designing and monitoring the spaces, policies and procedures within emergency shelters, and ensuring staff are trauma-informed and trained on the issues of sex trafficking can mitigate the risk of traffickers luring young people onsite. Interventions that support socio-economic inclusion and create supportive relationships with family and communities of belonging should be prioritized by service providers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".