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Record W3206162579

Ecofeminism in Atwood's Surfacing

2018· article· en· W3206162579 on OpenAlexaboutno aff
Priyanka Gupta

Bibliographic record

VenueInternational journal of research in social sciences · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsEcofeminismNatural (archaeology)EnvironmentalismFeminismFemininityMindsetPower (physics)DemotionEnvironmental ethicsSociologyLiteratureGender studiesHistoryPhilosophyEpistemologyArtPolitical scienceLawArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The most important problem that an individual faces today is the demotion of land and environment and its consequences on human presence. In this conditions the term Ecofeminism becomes highly relevant. This paper attempts to take an in-depth study of Margaret Atwood's Surfacing (1972) from an ecofeminist mindset. Within this text, power and domination, directly afflict both the womanly world and the natural world. The nameless idol of the story is an ecofeminist who returns to the undeveloped island, Northern Quebec, where she grew up, to search for her missing father. The protagonist realizes the gap between her natural inside her and her artificial construction only when she encounters true nature at most. The ecofeminist impact is seen implicit in the novel by the protagonist's return to the natural world. Her association with nature raises her awareness of deception of women. Like a true ecologist, she makes the earth her literal home which she knows that in the natural world all life is inter-related, brimming with diversity and complexity. Since the novel introduces issue pertaining to feminism and environmentalism, the novel constitutes a representative literary example of ecological feminism. Even the language, events and characters in this novel reflect a world that oppresses and dominates both femininity and nature.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.321
GPT teacher head0.495
Teacher spread0.174 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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