Imagining Beyond Extinctathon: Indigenous Knowledge, Survival, Speculation – Margaret Atwood’s and Ann Patchett’s Eco-Gothic
Bibliographic record
Abstract
Both Margaret Atwood and Ann Patchett engage with issues concerning indigenous knowledge, biodiversity, and survival. Margaret Atwood constructs a form of wilderness Gothic in Surfacing (1972) and Survival (1972); while in her darker eco-Gothic texts, The Handmaid’s Tale (1985) and the MaddAddam trilogy (Oryx and Crake [2003]; The Year of the Flood [2009]; MaddAddam [2013]), she focuses on survival post holocaust. Atwood’s work is influenced by indigenous knowledge and the awareness of imminent disaster should people fall out of harmony with nature, a threat enacted in these Canadian eco-Gothic dystopian fictions. This threat of extinction, of natural disaster based on arrogantly, deliberately, or accidentally ignoring the importance of ecological diversity and balance, informs much of Atwood's writing. Her work emphasises contestation, different voices and ways of being, throughout her writing career and her everyday life. Indigenous knowledge also interests many other women writers, including Ann Patchett from the US (State of Wonder [2011]), Alexis Wright from Australia (The Swan Book [2013]), Patricia Grace from New Zealand, (Baby No-Eyes [1998]), and Nalo Hopkinson from Jamaica/Toronto (“A Habit of Waste” [2001]); each of whom recognizes the importance of diversity, explores threats to survival, and suggests ways forward. Several of these writers, including Ann Patchett, evidence Atwood’s influence on a younger generation of women writers. In this essay, I link Atwood’s work to that of Ann Patchett, specifically to her novel State of Wonder, which problematizes the involvement of nonindigenous with indigenous people and their tribal behaviors, beliefs, and the rich forest and jungle worlds where they live in balanced harmony. Atwood and Patchett bring gender and sustainability issues to the fore by their use of eco-Gothic, emphasizing the damage done to natural processes (including fertility) by exploitation and unnatural controls. Both authors highlight lessons to be learned from indigenous values, behaviors, and wisdom, without underestimating the difficulties of translation, and the vulnerability of the peoples and their environments. Each shows the damage of their misuse or loss.
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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 | 0.003 | 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 | 0.018 | 0.036 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".