Problematizing the Educational Messaging on Sex Trafficking in the US “End-demand” Movement: The (Mis)Representation of Victims and Anti-Sex Work Rhetoric
Bibliographic record
Abstract
This study adopts a critical discourse analysis (CDA) approach to problematize the representation of victims in the online educational messaging on sex trafficking promoted in the US “end-demand” movement. The websites of 20 US anti-trafficking groups are analyzed. While these website-based messages are positioned to educate the public about sex trafficking, they are predominately framed toward problematizing sex work and essentializing women with racialized and marginalized identities in sex work, with no discursive recognition of intersectional structural inequalities (e.g., racism, sexism, poverty, homo/transphobia) that lead to trafficking. These ideologically charged messages, when presented as “facts,” further the anti-sex work sentiment among the public, powerfully (re)produce and sustain the public (mis)perception equating “anti-sex trafficking” with “anti-sex work,” and legitimize the carceral feminist anti-trafficking practice that primarily criminalizes, censors, and oppresses the agency, behaviors, and needs of structurally marginalized communities. This paper calls attention to how injustice may be (re)produced in the way trafficking is represented and how representational injustice may translate into material consequences, further subjecting already marginalized groups to criminalization and surveillance. Through incorporating representational justice into our conceptualization of racial and social justice, we may (re)build an anti-trafficking framework that is structurally competent, rights-inclusive, and centered on humanization.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".