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Record W2564471285

A Reflection of Reality? The Consumption and Reproduction of Obesity Discourses by The Biggest Loser’s Viewers Through Facebook

2016· article· en· W2564471285 on OpenAlexaff
Sarah Gray, Courtney Szto

Bibliographic record

VenueSocial Media + Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsSimon Fraser UniversityUniversity of Toronto
Fundersnot available
KeywordsConsumption (sociology)Social mediaPremiseMedia consumptionAdvertisingAction (physics)ObesityResistance (ecology)Reality televisionSociologyMass mediaMedia studiesPsychologySocial psychologyPolitical scienceMedicineSocial scienceBusinessLaw
DOInot available

Abstract

fetched live from OpenAlex

The Biggest Loser promotes itself as an avenue for ‘obese’ individuals to lose weight through exercise and diet modification with the end goal of a ‘healthy lifestyle’ (NBC, 2013). A driving premise behind The Biggest Loser is the idea that Western countries are in the midst of an ‘obesity epidemic’ and immediate action by all citizens is required. The purpose of this study was to provide insights into viewers’ consumption of the obesity discourses reproduced by The Biggest Loser, through the social media platform Facebook. Viewers’ Facebook posts were analyzed and categorized under the theoretical framework of biopedagogy. Data analysis observed that viewers’ Facebook posts reproduced obesity discourses concerning children, active or inactive citizens and inequalities. Facebook enables active participation of body surveillance and also serves as a multiplier of surveillance. The authors maintain that social media is a form of cultural texts; these texts reflect broader cultural understandings. Although some scholars argue that social media can facilitate movements of resistance, the authors observed that The Biggest Loser viewers reproduced obesity discourses.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
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 head0.166
GPT teacher head0.424
Teacher spread0.258 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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