Bhaji, Curry, and Masala: Food and/as Identity in Four Films of the Indian Diaspora
Bibliographic record
Abstract
This essay studies the connections between food (as material object and metaphor) and identity and investigates the representation of the Indian diaspora in films from four countries and two continents: Srinivas Krishna’s Masala (Canada, 1991) and Mira Nair’s Mississippi Masala (US, 1991) in North America; Gurinder Chadha’s Bhaji on the Beach (UK, 1993) and Vijay Singh’s One Dollar Curry (France, 2004) in Europe. The paper conducts a comparative analysis of the films, exploring the variations in the representation of the identities of Indians in films set in Europe and North America, i.e. the US and Canada. It examines how the masala of Nair’s Mississippi Masala is different from the masala of Krishna’s Masala. It examines how the films recreate the homeland or reject the recreation of the homeland in the diaspora. It questions if the food and spices in the films’ titles are mere marketing tools, a link to the homeland, or potent metaphors that describe the mixtures, the blending, the in-betweenness, the hybridization of the Indian diasporic characters in the films. Finally, it analyzes how the historical, political, and socio-cultural contexts of the adopted home influence the identity formation and assimilation of Indian diasporic migrants in the films.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".