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Record W2288524172 · doi:10.3968/8196

The Construction of Female Subject Identity in The Grapes of Wrath

2016· article· en· W2288524172 on OpenAlexvenueno aff
Limin Wu

Bibliographic record

VenueStudies in literature and language · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Literature and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)Identity (music)DualismEssentialismConstruct (python library)Consistency (knowledge bases)Order (exchange)PerfectionSociologyGender studiesAestheticsPsychologyEpistemologyArtPhilosophyComputer science

Abstract

fetched live from OpenAlex

The description of those females in The Grapes of Wrath by John Steinbeck is very impressive, among whom Ma Joad is depicted in such a way as one of the most important figures for the whole story. Though sharing some similarities with the traditional women in the past English novels, Ma Joad is very different. The close connection with as well as differences from the traditional gender role pattern are combined in this woman. It is just through the newly-opened window of differentiation that the reader can reconsider females and construct a new female identity. Mainly from an ecofeminist angle, the paper is going to study the consistency to and differences from the traditional gender roles of women exhibited in the novel, expound on the changing subject identity of women throughout the novel, in order to prove that female subject identity in the novel is beyond essentialism and dualism.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

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 studies0.0090.018
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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 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
Published2016
Admission routes1
Has abstractyes

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