Food, Family and Cultural Identity: Culinary Nostalgia and the Immigrant Experience in Venba
Bibliographic record
Abstract
Title: Food, Family and Cultural Identity: Culinary Nostalgia and the Immigrant Experience in VenbaAuthor: Dr Lakshmi MenonFood is among the most important memories immigrants carry with them to their adopted lands. The act of cooking traditional food helps members of immigrant communities to maintain their cultural identities in spaces that are far from the familiar and therefore food becomes a symbolic connection to their roots. This paper is a study of the 2023 video game Venba, developed by Visai games, which is a narrative driven independent game that tells the story of a Tamil immigrant family in Canada. Venba foregrounds the act of cooking as being central to the creation and preservation of cultural identity, through gameplay that blends the preparation of traditional Tamil dishes with storytelling that conveys the emotional journey that the characters undergo through the course of the narrative. By placing food at the centre of the family narrative, Venba engages with some central ideas of the diasporic relationship with food that is often explored in fiction. The purpose of this paper is to examine Venba’s narrative and its gameplay mechanics to see how it engages with the idea of food cultures as important points of connection with the homeland for diasporic communities. Through the story of an immigrant family and their relationships not only with each other but also with food, this paper will also look at the way the game engages with the concept of “culinary nostalgia” found in diasporic literature.Keywords: food, nostalgia, diaspora, culture
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".