Project Backpage: Using Text Messaging to Initiate Outreach Support for Victims of Human Trafficking and Sexual Exploitation
Bibliographic record
Abstract
The sex industry occurs in many venues, ranging from the highly visible survival sex trade on the street to venues such as regulated massage parlors and strip clubs, to less visible escort agencies making use of hotel venues, and the highly invisible exploitation that occurs through trick pads and microbrothels operating out of homes, apartments, or condos. In many communities, outreach efforts have focused primarily on direct face-to-face contact with individuals in the survival sex trade. However, in recent years, there has been a marked decrease in the street survival sex trade as the use of the Internet to buy and sell sites has become widespread. In this article, we describe the design and outcome of a multiphase community-university collaboration in Edmonton, Canada, to explore the use of Short Message Service (SMS) text messaging to initiate outreach with individuals advertising on the adult services section of the Web site Backpage.com. The article describes the impetus behind the project, the project goals and design, as well as results thus far. We also reflect on the results and present a set of emerging best practices, including the contribution of two-way text-based interaction for establishing trust between outreach organizations and the individuals seeking support. Overall, results from the project provide evidence to show that SMS is a cost-effective and important complementary communication strategy for outreach organizations seeking to initiate outreach to victims of human trafficking and sexual exploitation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".