Exposing Widow’s Psyche in a Fine Balance: A Study of Rohinton Mistry’s Widow Characters
Bibliographic record
Abstract
Indian writers including both male and female novelists generally utilise novels to reveal the psychological conditions of the female sex with the help of their dramatic personas. One such Indian writer is Rohinton Mistry, who unveils the mental states of women through his independent female characters, especially fictional female singletons. Among his numerous works, A Fine Balance is a notable piece that falls into this category. The novel is about the life struggles of the widow heroine Dina Dalal after the death of her husband. Apart from Dina, Mistry has used many widow characters who play minor roles in developing the storyline. The paper aims to exhibit the mental fluctuations of the fictional widows that comprise the widow protagonist Dina Dalal. The investigation with the help of the female characters uncovers the psychological oscillations of the widows due to their singlehood statuses. It unmasks the emotional transpositions, loneliness, fears, regrets, hopelessness and mental instabilities of the widows. The analysis avails the psychoanalytic Literary Theory to support its arguments and to obtain its objectives. With the aid of the select prose narrative, the research brings out Rohinton Mistry’s typical representation of widow characters to have psychological problems because of losing their husbands. Hence, the article projects that despite picturing the fictional widows as persons who are bold and liberating, Mistry has represented them to be psychologically vulnerable rather than presenting them as mentally strong and stable individuals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".