Study of Margaret Atwood’s Writing Study in Effect on Feminist
Bibliographic record
Abstract
Through Margaret Atwood's works, the research explores the woman's struggle for survival as it defines her role as a woman in modern society. Her books deal with the theme of survival as shown by the female characters. Her books' major themes include failure and fertility, multiculturalism, nature vs humanity, the search for one's own identity, Southern Ontario Gothic, unlikely legends, urban versus rural, and women's empowerment. She has tried to include all of her experiences as a woman, a female, and an essayist because she is recognized as a women's movement author of the 1960s. Patriotism has a strong hold upon Atwood. Her sense of feminism, patriotism and both the Canadian and female characters is also connected to her feminism. Her fundamental compositions reveal her awareness of gender and struggle for existence. Her characters and their personalities are subtly revealed in her stories, which transport the reader to a previous memory through which the present is seen and experienced. Atwood controls women's comfortable and realistic interactions in her books, and she creates a self-portrait of women as artisans and legends who are reliable to their own internal paths. She paints a portrait of a legendary figure whose persona doesn't require depth or tenderness. The protagonists and the novels have a remarkable relationship that allows for the recording of their emotions and dreams.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".