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Record W3111492726 · doi:10.32920/cd.v5i1.1335

Biomedicalized food culture

2020· article· en· W3111492726 on OpenAlexvenueaboutno aff
Myriam Durocher

Bibliographic record

VenueJournal of Critical Dietetics · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsFood cultureFood studiesSituatedSociologyFood processingCommercializationEnvironmental ethicsPolitical scienceMarketingAnthropologyBusiness

Abstract

fetched live from OpenAlex

This article presents my analysis of what I call the contemporary “biomedicalized food culture”. This food culture participates in defining the ways by which “healthy” food is currently understood and practiced, and in creating and orienting particular relationships between bodies and food. In this paper, I present Clarke et al.’s (2010) works on biomedicalization along with the works of researchers in critical food studies (such as Guthman (2014); Landecker (2011); Scrinis (2013)), which have inspired my analysis of the biomedicalized food culture. Inspired by Clarke et al.’s (2010) ways of presenting the biomedicalization of the social field, I present the contemporary biomedicalized food culture from and through its constitutive processes. Drawing from my fieldwork in Montreal, Canada, I discuss how mediatization, molecularization and commercialization processes participate in the development of the biomedicalized food culture as well as in the creation of knowledge and practices constitutive of “healthy” food, bodies, and the links between them. I approach this culture from a cultural studies’ perspective, which makes it possible to question the power relationships at stake in its development. I thus criticize how the biomedicalized food culture contributes to the (re)production of exclusions, discriminations, stigmatizations of some knowledge, practices and individuals, as well as to the (re)production of injunctions and normativities linking food, bodies and health, in particular and situated ways at the intersection of its constitutive processes. I finish up by opening up the discussion on how these relationships between food, bodies and health should be thought in their multiplicity and their complexity.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.602
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.269
Teacher spread0.227 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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