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Record W2246529221 · doi:10.7202/1062357ar

A Paler Shade of Green: Suburban Nature in Margaret Atwood’s Cat's Eye

2019· article· en· W2246529221 on OpenAlexaffvenueabout
Robert Ross

Bibliographic record

VenueStudies in Canadian Literature · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsWildernessJungleAsideNarrativeSociologyNatural (archaeology)AestheticsEnvironmental ethicsHistoryArtEcologyLiteratureArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Critics of Canadian literature such as Cheryl Cowdy, Frank Davey, and Franca Bellarsi construe suburbia as existing somewhere in between the concrete jungle and the verdant wilderness. The ecocritical implications of this geographic and critical positioning, however, have not yet been thoroughly examined. Common images of suburbanites portray people in the “enclosed private worlds of fences, parlours and automobiles,” cut off from their larger communities and environments in collective isolation. Margaret Atwood’s Cat’s Eye (1988) depicts how this separate-from-nature culture is fostered. As Elaine Risley faces the repressed, traumatizing experiences of her childhood, she confronts her and her society’s various interrelationships with the natural world, showing how a suburban upbringing can produce unsatisfactory relationships with both human and non-human nature. In so doing, Cat’s Eye critiques common, urbane conceptions of nature from a point of view that is quintessentially ecocritical. Aside from the obvious environmental concerns vocalized by Elaine’s biologist father, ecological issues are relevant to three other aspects of the novel: Elaine’s early childhood in northern Ontario, her later summer vacations there, and the social pressures and cultural practices that Elaine experiences in suburbia. Through these elements of the narrative, Cat’s Eye articulates some of the fundamental relationships with nature experienced by those living in suburban Canada and seeks to move beyond conventional portrayals of this relationship.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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 teacher head, not a consensus.

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
Published2019
Admission routes3
Has abstractyes

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