Beyond A Gastronomic Expedition: Reading Chitrita Banerji’s A Taste of My Life as Food Talk
Bibliographic record
Abstract
Women throughout history have had an enduring association with food, but it is only with the advent of food studies that the multi-faceted role food has played in women’s lives has started getting documented. Food studies has ventured beyond its focus on food in recipe books as a vehicle to satisfy physiological requisites and had begun to consider them as narratives that record women’s voices. Women through archiving recipes in their works become an active contributor as well as transmitter of their culinary heritage. They publicize their traditions and blend it with their personal stories thereby becoming culinary custodians. The memoir A Taste of My Life by Chitrita Banerji lays emphasis on the significant role that women play in preserving and disseminating culture. Being an Indian expatriate writer living in the United States, Banerji’s works are usually studied in the light of transnational perspectives for their role in addressing the issues of identity and diaspora. This paper focuses on reading the memoir from a feminist food perspective. It questions the trivialization of woman’s role in the transmission of culture, in the light of women’s centrality to food practices and attempts to read Banerji’s memoir as a valuable document of woman’s history. The evocative memoir is analysed as a medium through which Banerji engages in a food talk with her readers which triggers their imagination and personal experiences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".