Dismantling silos: cross-sectoral response to combating child sex trafficking and online child sexual exploitation
Bibliographic record
Abstract
Until recently, child sex trafficking and online child sexual exploitation were treated as distinct crimes against children, leading to siloed responses from victim services, legal and policy frameworks, and law enforcement. Technology has blurred this distinction, and a cross-sectoral and multi-disciplinary response is needed as a first step to protect and respond effectively to victims. This paper presents the findings from a symposium in Toronto, Ontario, Canada in June 2024. Children’s mental health and child welfare practitioners, researchers, policymakers, child advocates, law enforcement professionals, and community members with lived experience came together to explore the current state of knowledge, policy, and practice related to technologically-assisted child sex trafficking and exploitation and gaps in responses to child victims. Using thematic analysis, we identified two overarching themes. Sector-Specific Roles & Challenges highlights the roles, challenges, and gaps across five key stakeholder domains. Cross-Sector Imperatives underscores the institutional, survivor-, and child-led leadership required to move beyond silos. We conclude with recommendations across policy, practice, and research to build coordinated, survivor-centered support systems capable of meeting the challenges of emerging technologies.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".