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Record W4311012296 · doi:10.1080/14680777.2022.2149592

There’s a trafficking jam on the underground railroad: black abolitionist icons and anti-trafficking media

2022· article· en· W4311012296 on OpenAlexaff
Lyndsey P. Beutin

Bibliographic record

VenueFeminist Media Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsUnderground RailroadState (computer science)CriminologyPolitical sciencePoliticsSociologyLawMulticulturalismReverence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.319
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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