Anthropocene Fiction: Empathy, Kinship, and the Troubled Waters at the End of the World
Bibliographic record
Abstract
Anthropocene literature features characters and environments grappling with the devastation of climate change. Donna Haraway’s term Chthulucene narrows this category further by exposing the tentacular connections between overpopulation, dependence on material goods, species loss, and other facets of global warming—such as water as a resource. Three novels of the Chthulucene exhibit these connections, centering on the importance of water to both ecologies and personal identity. As seen in the novels, place attachment brings ecology and identity together by forging a stewardship between person and location, often resulting in efforts to defend and preserve a place. In Octavia Butler’s Parable of the Sower (1993), protagonist Lauren Olamina navigates a burning and destroyed California to build Earthseed, a collaborative community situated in diversity and resource sharing that serves as a model for how to survive climate change. Lauren’s Hyperempathy Syndrome makes her feel what others around her feel, such as pain, and illustrates how empathy—for humans and nonhuman nature alike—can create positive change. Linda Hogan’s Solar Storms (1994) depicts the environmental threat of hydrodams along the border between the U.S. and Canada to Indigenous communities. With this threat looming, 17-year-old Angel Jensen shares a water journey with women of her ancestral lands that strengthens her attachment to nonhuman nature and bolsters her courage to resist the dam project. In Nnedi Okorafor’s Lagoon (2014), humans join animals, plants, monsters, and aliens in a fight against the forces of planetary destruction. Set in Lagos, a bustling epicenter of oil production and social upheaval, this Africanfuturist novel prioritizes non-Western, nonhuman voices while representing the ocean, with all its substances and fluidity, as a symbol for climate change. Ultimately, these novels send a hopeful message for fighting climate change and for thriving amidst its effects we are already witnessing. Adaptability proves key to each of these messages: Earthseed materializes only after Lauren sees her walled neighborhood burn; Angel becomes a leader in the struggle against damming after leaving her home in Oklahoma and adopting the ecological philosophies of her foremothers; Adaora, Agu, and Anthony of Lagoon must grapple with the newly arrived aliens’ demands and the transformations of their individual abilities. In each novel, adaptability, change, and hope are intertwined. The positive messages of each novel lend themselves well to applications in the classroom. Students can learn from the power of adaptability in the Chthulucene to improve their own communities and ecosystems around the globe.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".