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Record W2106494350 · doi:10.5539/ells.v3n1p31

A Measure of Victory: Go Down, Moses and the Subversion of Racial Codes

2013· article· en· W2106494350 on OpenAlexvenueno aff
Abdul-Razzak Al-Barhow

Bibliographic record

VenueEnglish Language and Literature Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVictorySubversionWhite (mutation)NarrativeStatus quoIdeologyOrder (exchange)Measure (data warehouse)Race (biology)MainstreamSociologyGender studiesHistoryPolitical sciencePoliticsArtLiteratureLawComputer science

Abstract

fetched live from OpenAlex

This article studies the way white and black characters in Go Down, Moses engage with the racial codes inside the McCaslin plantation. It tries to examine the impact of William Faulkner's engagement with social change and race relations on the structure of the seven stories that make up the text of Go Down, Moses. It argues that there is a measure of triumph in the continuous attempts by both black and white characters at subverting the racial order in the southern plantation even though these attempts are quite often subverted by counter attempts at maintaining the status quo. The book achieves a “measure of victory” in the way its narrative techniques acknowledge and respect the otherness of black people and, more importantly, in the way its white and black characters maintain their determination to subvert the codes of the Southern racial ideology.

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)0.012
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 score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.025
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.207
Teacher spread0.200 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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