Exploring the Ecofeminist Landscape: A Comparative Analysis of Margaret Atwood’s Surfacing and Gita Hariharan’s The Thousand Faces of Night
Bibliographic record
Abstract
Ecofeminism is a theoretical framework that examines the parallels between the exploitation of the environment and the subjugation of women. The existing research in the field of ecofeminism concentrates on a specific regional level. But a critical, constructive, comparative study across geographical borders is least present in this field. To address this scholarly gap, the current research focuses on a textual comparative analysis of a well-known Canadian writer, Margaret Atwood’s Surfacing and an Indian writer, Githa Hariharan’s The Thousand Faces of Night, through the lens of ecofeminism and post-colonialism. The analysis reveals how the authors challenge patriarchal norms and advocate for the empowerment of women and the preservation of the natural world. Atwood’s protagonist exemplifies cultural ecofeminism while Hariharan’s characters embody socialist ecofeminism. The current study significantly contributes to ecofeminist literary criticism by comparing a developing nation and a developed country, which is least represented in the existing ecofeminist literary discourse. The article demonstrates how Atwood presents the protagonist’s actions as a conscious political decision rather than a biological determination, and it integrates ecofeminist concerns through the symbolic merger with the wilderness. Hariharan explores a post-colonial world where mythology and modernity intersect, using mythology to reveal structural oppression and the resistance of women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.041 | 0.026 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".