Gendered lives in vernacular fiction: Redefining family in Hindi short stories of the early 1940s
Bibliographic record
Abstract
This article is embedded in discourses surrounding the new mobility of people as well as scientific, technological and socio-cultural changes in a late-colonial setting. It investigates how a number of prominent and less-known male authors from the centre and margins of the twentieth-century Hindi literary canon, including Rishabhcharan Jain, Shriyut ‘Arun’ and Durgadas Bhaskar, depict unconventional family constellations and human relationships that challenge normative conceptions of family, fatherhood, conjugality and blood bonds as well as gender roles and responsibilities. The short stories under investigation suggest that human relationships require constant negotiation and investigation of the meaning of kinship, caste, class and the human. In the process, we encounter adulterous husbands, strong wives and nurturing fathers’ life struggles and tribulations. These short stories centre on husband–wife, man–mistress, wife–mistress and father–son relationships. Their male protagonists are authoritative towards their wives, caring towards their mistresses and nurturing towards children. At times, their self-sacrifice goes as far as to complete self-annihilation for the sake of the offspring, and, at other times, they lead double lives. Mothers are absent in these short stories. Instead, male protagonists claim parenthood and are ready to go as far as to abduct infants in order to perform fatherhood. I argue that parenting constellations and conjugality became negotiable for a number of factors that are addressed in my selection of Hindi short stories: (a) parenthood was not contingent upon biology (as stories on adoption and abduction suggest), (b) contraception was readily available to women and men (as promoted in periodicals of the time) and in the process also changing attitudes towards sexuality and conjugality, (c) abortion emerged as a medical option to undo a pregnancy emerging from an illicit love affair and (d) the new mobility enabled people to get around easily and frequently and even lead double lives. In addressing these factors, fiction published and circulated in periodicals offered novel imaginative and innovative spaces for the negotiation of family models once projected as normative in social reformist and nationalist discourses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".