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Record W4229773884 · doi:10.4000/books.pulm.10198

Bhaji, Curry, and Masala: Food and/as Identity in Four Films of the Indian Diaspora

2011· book-chapter· en· W4229773884 on OpenAlexaboutno aff
Binita Mehta

Bibliographic record

VenuePresses universitaires de la Méditerranée eBooks · 2011
Typebook-chapter
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsHomelandDiasporaGender studiesIdentity (music)Media studiesGeographySociologyPoliticsGenealogyHistoryArtAestheticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.198
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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