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Record W2996013972 · doi:10.29333/ejecs/301

The Underground Railroad As Afrofuturism: Enslaved Blacks Who Imagined A Future And Used Technology To Reach The “Outer Spaces of Slavery”

2019· article· en· W2996013972 on OpenAlexaboutno aff
Dann J. Broyld

Bibliographic record

VenueJournal of Ethnic and Cultural Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsUnderground RailroadRace (biology)TactRealismInstitutionMagic realismWhite (mutation)HistoryAestheticsSociologyArtLawArt historyGender studiesVisual artsPolitical scienceSocial sciencePsychology

Abstract

fetched live from OpenAlex

This article employs the lens of Afrofuturism to address the Underground Railroad, detailing what imagination, tact, and technology, it took for fugitive Blacks to flee to the “outer spaces of slavery.” Black enslavement was as terrifying as any exotic fictional tale, but it happened to real humans alienated in the “peculiar institution.” Escaping slavery brought dreams to life, and at times must have felt like “magical realism,” or an out-of-body experience, and the American North, Canada, Mexico, Africa, Europe, and free Caribbean islands were otherworldly and science fiction-like, in contrast to where Black fugitives ascended. This article will address the intersections of race, technology, and liberation, by retroactively applying a modern concept to historical moments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0160.025
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
GPT teacher head0.308
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations38
Published2019
Admission routes1
Has abstractyes

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