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Record W2757425818 · doi:10.3138/cras.2017.022

Why Is Melanctha Black?: Gertrude Stein, Physiognomy, and the Jewish Question

2017· article· en· W2757425818 on OpenAlexvenueno aff
Yeonsik Jung

Bibliographic record

VenueCanadian Review of American Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysiognomyHatredRacismJudaismPseudoscienceLesbianContext (archaeology)Power (physics)Identity (music)Jewish identitySociologyGender studiesLiteraturePsychoanalysisArtHistoryAestheticsPhilosophyPsychologyAnthropologyTheology

Abstract

fetched live from OpenAlex

This article reads Gertrude Stein’s “Melanctha” as a racial, if not racist, text—a text not solely about blacks but also about Jews in the sense that black characters work as a mask for the author’s concern about her own Jewishness. Nineteenth-century pseudoscientific medical discourses link blackness, Jewishness, and homosexuality—the trinity of “difference”—based on their biological connectedness and similarities. Examining the racism and misogyny particularly inherent in the theories of physiognomy, this article will demonstrate the ways in which Stein employs and manipulates representative prejudices of her time in the portrayal of female characters in Q.E.D., as well as the black Melanctha, through which she reveals and conceals her own racial and sexual identity as a Jewish-American lesbian woman. Arguing that the aesthetic experimentation of “Melanctha” grows out of the author’s concerns about her racial and sexual marginality, this article further offers a context in which the black Melanctha may be read as a character Stein created to treat her racial self-hatred, a problematic phenomenon exhibiting the productive power of fear.

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 categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.007
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.336
Teacher spread0.304 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2017
Admission routes1
Has abstractyes

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