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Record W6990621694

Duality of victim/victimizer in personal and national context in Margaret Atwood’s Surfacing

2022· dissertation· en· W6990621694 on OpenAlexaboutno aff

Bibliographic record

VenueDSpace repository (University of Tartu) · 2022
Typedissertation
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Duality (order theory)PropositionKey (lock)
DOInot available

Abstract

fetched live from OpenAlex

Margaret Atwood's Surfacing, first published in 1972, is a novel about a woman's journey both in the physical and metaphysical sense.The thesis explores duality in Atwood's writing focusing especially on the duality of victim/victimizer in Surfacing.The aim of this thesis is to analyze how this duality can be perceived as the protagonist expresses her views of herself and Canada and how these views transform in the course of the novel.The thesis consists of an introduction, two main chapters, and a conclusion.The introduction gives background information on the novel, its importance in the Canadian context, and Atwood's use of duality.The introduction also includes the aim of the thesis.The first main chapter is a literature review that provides an overview of duality in Atwood's writing as discussed in literary criticism, and the thematic dualities of female/male, nature/culture, and victim/victimizer Atwood has used in Surfacing.The second main chapter provides an analysis of the victim/victimizer duality in the novel.The chapter is divided into two subchapters that focus on the transformation of the protagonist's view of herself and the transformation of the protagonist's view of Canada respectively.The conclusion summarizes the main findings of the thesis.

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)0.004
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 score0.340
Threshold uncertainty score0.675

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0280.030
Scholarly communication0.0120.004
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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