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Record W4393232794 · doi:10.1080/15528014.2024.2334094

“How authentic is your curry”? performing curry and diasporic identity in Naben Ruthnum’s <i>Curry: Eating, Reading, and Race</i>

2024· article· en· W4393232794 on OpenAlexaboutno aff
Sanghamitra Dalal

Bibliographic record

VenueFood Culture & Society · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsCurryReading (process)Identity (music)PsychologyRace (biology)Developmental psychologyLinguisticsSociologyGender studiesArtComputer sciencePhilosophyAesthetics

Abstract

fetched live from OpenAlex

In this article, I examine Indo-Mauritian-Canadian author Naben Ruthnum’s memoir-essay Curry: Eating, Reading, Race (2017) in order to examine the veracity of the dominant perception of South Asian diasporic identity as a collective designation through its association with the ubiquitous dish of curry which embodies a predominant cultural signifier of an extensively diverse population. Ruthnum’s significant aim is to challenge the existence of a supposedly authentic Indian curry and also to question the risk-averse publishing industry which solicits stories steeped in stereotypically authentic and nostalgic experiences from the second or third generation South Asian diasporic authors. Through an exploration of many ideas of authenticity and multiple ways of cooking the diverse dish of curry, I argue that diasporic authenticity is more appropriately performed not through replication and preservation of the past, but through constant recreation and reinvention of an individual’s present predicaments. Authenticity in diaspora is, therefore, unique and individual, and an embodiment of personal history. As there are many truths to the same story, and many versions of the same story, there are multiple ways of cooking authentic curries and diverse modes of confronting one’s own self as a South Asian diasporic in the world.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes1
Has abstractyes

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