Gender and Surveillance in Margaret Atwood’s Novels, from Bodily Harm (1981) to The Testaments (2019)
Bibliographic record
Abstract
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 Bodily Harm (1981) builds on 1970s theorizations of the “male gaze” but complexifies them by articulating them with postcolonial perspectives. The Handmaid’s Tale (1985) and its sequel The Testaments (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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".