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Record W2910635192 · doi:10.22146/lexicon.v5i2.42068

Elaine Risley’s Character Development in Margaret Atwood’s <i>Cat’s Eye</i>

2018· article· en· W2910635192 on OpenAlexaboutno aff
Nur Afifah Widyaningrum, Eddy Pursubaryanto

Bibliographic record

VenueLexicon · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)GirlPsychologyEarly childhoodDevelopmental psychologyCharacter developmentPsychoanalysis

Abstract

fetched live from OpenAlex

This research explores the character development of Elaine Risley, the main character of the novel Cat’s Eye by Margaret Atwood (2009), throughout her childhood, adolescence, early adulthood, and adulthood years. The objectives of this research are to explain how the character of Elaine Risley develops in Cat’s Eye and to examine the factors which affect Elaine Risley’s character development. This research employs the objective approach proposed by Abrams (1976) as its theoretical framework and the library research as its method of research. The results show that Elaine Risley always experiences development in her character throughout her life; she develops from a bullied little girl in her childhood, a mean but passionate girl in her adolescence, and an independent young woman in her early adulthood to finally become a woman who struggles to let go of her past in her adulthood. Elaine Risley’s character development is affected by several factors, namely, Toronto as her environment, her experiences with bullying, the men and women in the society around her, her own paintings, the cat’s eye marble, and the Virgin Mary.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.015
GPT teacher head0.230
Teacher spread0.214 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

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