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Record W2313857639 · doi:10.1017/s0026749x11000850

Between Digestion and Desire: Genealogies of food in nationalist North India

2013· article· en· W2313857639 on OpenAlexaff
Rachel Berger

Bibliographic record

VenueModern Asian Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsConcordia University
Fundersnot available
KeywordsHinduismColonialismNationalismPoliticsHindiNorth indiaReproductionSociologyGender studiesHistoryGeographyPolitical scienceEthnologyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract This paper takes up the project of conceptualizing a new history of food in India through an exploration of conversations about food, digestion, desire, and embodiment that took place in Hindi-language publications in early-twentieth century North India. Through an exploration of cookbooks, guides to health and wellness, and food advertising spanning the 1920s to the 1940s, conversations about food preparation, consumption, and distribution come to be revealed as significant anchors of historical, political, economic, and cultural debates about the Indian nation in this period. The centrality of food to conversations that took up the reproduction and regeneration of the Hindu middle class helped to conceptualize an idealized Indian nation[A]. Subsequently, the focus on food advertising imagined the transformation of these citizens into consumers. Moving beyond the colonial fascination with native bodies and tropical constitutions, this paper demonstrates the ways in which the conversations that emerged out of a focus on food in popular culture did the work of envisioning new possibilities for post-colonial embodiment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.020
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.089
GPT teacher head0.336
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations11
Published2013
Admission routes1
Has abstractyes

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