Ecology of Self: woman’s self-discovery in Margaret Atwood’s <i>Surfacing</i>
Bibliographic record
Abstract
The present paper discusses the problem of a woman’s self-perception in the environment degraded by patriarchal power relations and the pressures of contemporary consumer society, as depicted in Margaret Atwood’s novel Surfacing (first published in 1972). Atwood’s extensive and diverse writing is characterised by themes such as Canadian national and cultural identity, history, human rights, new humanism, ecological thinking, feminism, and issues of feminine identity. Women characters in Atwood’s writing are affected by the brutality, blindness, and deadlock created by imperial, patriarchal, and ethnocentric power structures. Nevertheless, they are portrayed as capable of creative self-expression and rational self-reflection. Consequently, texts depicting a woman’s self-discovery are of special significance in Atwood’s writing. This is particularly evident in Surfacing, where the heroine engages in introspection regarding her family’s genealogy, traces the imposed deformations of her experience, seeks the foundations of authentic feminine experience, and ultimately gains insight into her transformed perception of herself and her environment. The process of self-discovery in Atwood’s novel is analysed in the framework of the French feminist school of écriture féminine, the theoretical perspectives of Alice Jardine and Rita Felski, as well as cultural ecology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".