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Record W1978161639 · doi:10.1080/21604851.2013.778166

Moving Subjects, Feeling Bodies: Emotion and the Materialization of Fat Feminine Subjectivities in<i>Village on a Diet</i>

2014· article· en· W1978161639 on OpenAlexafffundabout
Moss E. Norman, Geneviève Rail, Shannon Jetté

Bibliographic record

VenueFat Studies · 2014
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsConcordia UniversityUniversity of Manitoba
FundersConcordia University
KeywordsExpansiveFeelingCorporationSociologyReality televisionReality tvWork (physics)Gender studiesMedia studiesPsychologyAestheticsSocial psychologyPolitical scienceArtLaw

Abstract

fetched live from OpenAlex

Abstract In this paper, we conduct a critical feminist-informed poststructural analysis of the Canadian Broadcasting Corporation's reality weight loss series, Village on a Diet (VOD). We argue that the "problem of obesity" is felt into reality through the cultural work of emotions as represented in VOD. We further situate VOD as one node in a more expansive, interwoven network of discourses, sites, and technologies. In so doing, we argue that the felt force of "obesity" discourse is magnified as it circulates relationally throughout this network, materializing the felt "truth" about fatness (e.g., that it is unhealthy) and fat subjects (e.g., that they are unhappy, unsexy, bad parents). In this way, VOD serves a (bio)pedagogical function as it instructs—indeed, necessitates—that villagers and viewing Canadians alike work on and transform their bodies into leaner, supposedly more healthy forms as a means of striving towards the promise of a better life. Keywords: affectfatfemininityobesityreality tv ACKNOWLEDGEMENTS The authors would like to thank the Social Sciences and Humanities Research Council of Canada, the Canadian Institutes of Health Research, and Concordia University for funding their collective research programme.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.061
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.062
GPT teacher head0.396
Teacher spread0.334 · 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 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

Citations13
Published2014
Admission routes3
Has abstractyes

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