There’s a trafficking jam on the underground railroad: black abolitionist icons and anti-trafficking media
Bibliographic record
Abstract
This article demonstrates how anti-trafficking media use the public memory of the Underground Railroad to racially legitimize US global policing regimes. From far-right paramilitary vigilante groups to liberal multicultural public history institutions, the anti-trafficking industry’s reverence for 19th-century Black women abolitionist icons is mobilized, counter-intuitively, to build public support for carceral agendas. Through visual analysis of the media of two exemplar organizations—Operation Underground Railroad and the National Underground Railroad Freedom Center—I unpack the racial dynamics of anti-trafficking’s carceral humanitarianism and the racial politics of anti-trafficking’s memory of transatlantic abolition. I argue that incorporating icons of radical Black freedom struggle, such as Harriet Tubman, into anti-trafficking’s neoliberal carceral agenda becomes a racial alibi for the perpetuation of ongoing racialized state violence in the name of abolition. US policing is thus racially legitimized as a set of freedom-granting institutions amid the ongoing Black women-led freedom struggles that name policing’s role in perpetuating antiblack state violence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".