Bibliographic record
Abstract
This chapter analyzes Margaret Atwood´s dystopian trilogy MaddAddam , composed by the novels Oryx and Crake (2003 ); The Year of the Flood (2009); and MaddAddam (2013), by focusing on the juxtapositions of ethical, gender, and ecological issues. We look at the figuration of the major women characters, especially Oryx in the first novel, regarding the ways Atwood’s SF can be aligned to contemporary theories about women’s bodies, power (Foucault, 1994), and the mascarade (Russo, 1986; Grosz, 1990; Butler, 1990). By observing the functioning of the dystopian principle of world reduction (Jameson, 2005) in Atwood´s narrative, we stress the gender politics underlying the portrayal of the women characters as we deal with the following questions: how to understand the author’s problematizing of women’s trajectories in the context of the patriarchal ideology as still configured in the future depicted in the novel? To what extent do women’s bodies reenact a potential identification with the oppressor? What are the literary metaphors at play in the construction of the interactions within the human and the more-than-human (Alaimo, 2010) universe? Bearing in mind gender and ecological tropes, how ethical is the world created by the Canadian author? Viewed as SF, in the sense that it combines science fact, science fiction, speculative fabulation, and speculative feminism (Haraway, 2016), Atwood’s works may raise provocative reflections as regards the future of humanity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".