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Record W2289671518

Invisibility and the Commodification of Blackness in Ralph Ellison’s Invisible Man and Percival Everett’s Erasure

2010· article· en· W2289671518 on OpenAlexvenueno aff
Scott Thomas Gibson

Bibliographic record

VenueCanadian review of comparative literature · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsInvisibilityPostmodernismAestheticsHybridityErasureWhite (mutation)ArtSociologyLiteratureArt history
DOInot available

Abstract

fetched live from OpenAlex

This essay specifically examines Erasure as a postmodern rewriting of Ralph Ellison’s Invisible Man and as a revision of the modernist trope of Black invisibility so famously rendered by Ellison. Drawing upon poststructuralist theories of “race” as well as New Black Aesthetics theories of cultural hybridity, I contend that Erasure highlights modes of “invisibility” that persevere in contemporary American literary and consumer cultures. Unlike modernist invisibility that manifested itself in the fissures between Black and white in the biracial social taxonomy of the United States throughout most of the twentieth century, contemporary generic and aesthetic categories generate sublimated forms of invisibility that give the appearance of legitimizing heterogeneous African American subjects while simultaneously and subversively regulating the available range of supposedly “authentic” Black representations. In this process, the rich pluralism of Black culture and aesthetics gets reduced to a few subgenres that prove most economically viable and palatable to white and Black consumers alike.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.028
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.309
Teacher spread0.293 · 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 designTheoretical or conceptual
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

Citations3
Published2010
Admission routes1
Has abstractyes

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