Remembering to Forget: Wartime Mothers in Tahmima Anam’s <i>A Golden Age</i>
Bibliographic record
Abstract
This essay calls for a deeper engagement with the portrayal of wartime motherhood in literary and cultural representations of the Bangladesh Liberation War. Focusing on Tahmima Anam’s critically acclaimed novel A Golden Age, we delineate the simultaneous valorization and erasure of wartime mothers in the context of the dominant nationalist discourse on the Liberation War. The novel traces how the protagonist Rehana navigates the pressures of valiant motherhood thrust upon her and emerges as the celebrated male freedom fighter’s mother through her sacrifices for her son. But as the mother par excellence, Rehana also validates normative codes of gender performativity and effectively inhibits the memorialization of other mothers in the novel. Our analysis illustrates how the novel effectively marginalizes and erases from the narrative plot, and, in turn, from nationalist remembering mothers and women like Sharmeen who is a rape survivor and Supriya who is a Hindu refugee mother displaced from East Pakistan. Our hope is to interrogate the emphatic telling of the glorious birth of the nation made possible by the freedom fighter’s mother and open up spaces for the exploration of the multifaceted contributions by wartime women-as-mothers, who are routinely left out of national commemoration and public mourning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".