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Record W4290190659 · doi:10.53730/ijhs.v6ns8.11604

Childhood trauma and the identity quest in Margaret Atwood’s Cat’s Eye

2022· article· en· W4290190659 on OpenAlexaboutno aff
M. Muthulakshmi, S. Ganesan

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsPatriarchyPower (physics)PsychoanalysisIdentity (music)PoliticsGender studiesIdealismAuthoritarianismSociologyPsychologyLiteratureArtAestheticsLawPhilosophyDemocracyPolitical science

Abstract

fetched live from OpenAlex

Margaret Atwood, an iconic Canadian writer in many of her novels has dealt with the power politics and its consequences especially in her women protagonists’ life. In her novel Cat’s Eye, the power games are played exquisitely by little girls. As it has been done previously by Graham Greene and William Golding in their works, Atwood has effectively captured the complex relationship between the school bully and victim, through her characters. Like Atwood’s earlier novels, Cat’s Eye is a novel which is not only against to restrictive idealism, it is also against to authoritarianism manifesting in several forms. According to Atwood, women suffer not only at the hands of rigid patriarchy; even female folks indulge in bullying and torturing the less privileged of their own gender. In this novel, Elaine Risley was a victim and a protagonist. She is victimized by her three classmates at her school-Cordelia, Carol and Grace Elaine Risley persecuted from her child age and in her young age it increased a lot. Due to her itinerant life, she became homeless among her peers. Consequently, she became a victim to the surroundings.

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)0.005
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 score0.099
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0370.029
Scholarly communication0.0100.006
Open science0.0010.008
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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