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Gender and Surveillance in Margaret Atwood’s Novels, from Bodily Harm (1981) to The Testaments (2019)

2023· book-chapter· en· W4391119564 on OpenAlexaboutno aff
Claire Wrobel

Bibliographic record

VenueEdinburgh University Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsTrilogySubversionResistance (ecology)ForegroundingIronyMetisHarmHistorySociologyPower (physics)PsychoanalysisLiteratureGender studiesArtPsychologyLawPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

L Literary fiction, through its instantiation power, offersa privileged means to study surveillance as socially located and embodied, and as having differential impacts. Margaret Atwood’s fiction is particularly fertile ground, as it engaged with surveillance long before it rose to prominence in the wake of the 9/11 attacks, and has consistently focused on the gendered implications of surveillance over time. The chapter first shows how <italic>Bodily Harm</italic> (1981) builds on 1970s theorizations of the “male gaze” but complexifies them by articulating them with postcolonial perspectives. <italic>The Handmaid’s Tale</italic> (1985) and its sequel <italic>The Testaments</italic> (2019) have in common their foregrounding of the fact that being under surveillance is fundamental to the female experience, and of the possibility of forms of resistance which are local, embodied and dependent on the contingencies of human interaction. The MaddAddam trilogy (2003-2013), which registers advances in the field of surveillance such as biometrics and dataveillance, refuses technological determinism, shows the persistence of long-established patterns of surveillance rooted in patriarchy, and highlights the possibilities of resistance and subversion both on the fictional stage and in writing, using satire, humour and irony to undermine claims to ubiquity and omnipotence.

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: Other · Consensus signal: Other
Teacher disagreement score0.963
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 integrity0.0000.000
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 head0.064
GPT teacher head0.213
Teacher spread0.150 · 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
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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