SENSE OF ALIENATION AND EMOTIONAL ESTRANGEMENT –A COMPARATIVE STUDY OF ANITA DESAI AND MARGARET ATWOOD’S HEROINE
Bibliographic record
Abstract
The quest for identity and the struggle to identify oneself with the surroundings resulting in a sense of alienation and estrangement is experienced by the protagonists of the women's writing.This paper attempts to focus on the common features of the characters in the novels of the two women authors Anita Desai and Margaret Atwood.Most of the protagonists of Desai can be classified under two categories, one with disturbed, obsessed, oversensitive, aesthetic sensibility while the others are a little tough, stubborn, sarcastic, and cynical about the situations and the people around.Whether it is Maya, Monisha, Sita and Tara who belong to the first category or Nirode, Amla, Nanda and Sophie who belong to the second category, it all begins with the feeling of that estrangement which leads to the zenith though they chose different endings.Margaret Atwood opines that the central reality of Canada or Canadian literature depends wholly on the concept of survival and existence.Similar to those of Desai, Atwood's heroines attempt to discover their inner self as they feel that they are like emotional refugees seeking shelter in a terrain that they cannot identify themselves with.Most of her characters, especially women feel emotionally maimed and undergo a constant turmoil to connect with the now and present.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".