Bibliographic record
Abstract
This paper explores the posthuman and ecofeminist dimensions of Margaret Atwood’s Surfacing, with a focus on how nonhuman elements—specifically the landscape, animals, and water—emerge as active agents rather than passive backdrops. Drawing on theoretical frameworks from ecofeminism and posthumanism, including the works of Rosi Braidotti, Jane Bennett, and Donna Haraway, the analysis demonstrates how Atwood reconfigures human subjectivity by centering relationality, interdependence, and nonhuman agency. Through the protagonist’s psychological and physical immersion in the Canadian wilderness, Surfacing interrogates Cartesian dualisms such as human/nature, male/female, and reason/emotion. The forest becomes a sentient witness; the lake, a womb-like site of memory and truth; and animals, mirrors of human violence and empathy. As the protagonist sheds the layers of her culturally imposed identity, she enters into a liminal space where boundaries between species and selves collapse, enabling a posthuman reawakening grounded in ecological consciousness. The novel critiques patriarchal, capitalist systems that commodify both women and nature, while offering a vision of subjectivity rooted in reciprocity, embodiment, and non-dominance. This reading positions Surfacing not only as a feminist and environmental narrative but as a prescient text that anticipates contemporary discourses on multispecies ethics and the Anthropocene. Ultimately, Atwood invites us to listen to the voices of the nonhuman world—and in doing so, reimagine what it means to live ethically and sustainably within a shared planetary ecology.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".