Anti-trafficking saviors: Celebrity, slavery, and branded activism
Bibliographic record
Abstract
This article traces the development of popular forms of anti-trafficking activism in the United States through a social network and discourse analysis that focuses on NGO websites, celebrity advocacy, merchandising, social media campaigns, and policy interventions. This "branded activism," as we describe it, plays an important role in legitimizing an emerging anti-trafficking consensus that increasingly shapes both US foreign policy and domestic policing, and is frequently driven by an anti-sex work politics. Popular anti-trafficking discourses, we find, build on melodramatic narratives of victims and (white) saviors, depoliticize the complex labor and migration issues at stake, reinforce capitalist logics, and enable policy interventions that produce harm for migrants, sex workers, and others ostensibly being "rescued." Celebrity and marketing-driven branded activism relies especially strongly on parallels drawn between histories of chattel slavery and what anti-trafficking campaigns call "modern-day slavery." We challenge these parallels, particularly as they encourage participants to see themselves as abolitionist saviors in ways that reinforce neo-liberal notions of empowerment rooted in communicative capitalist networks.
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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 | 0.002 | 0.004 |
| 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 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".