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Record W2767952370 · doi:10.1080/21604851.2017.1374696

“You can only be happy if you’re thin!” Normalcy, happiness, and the lacking body

2017· article· en· W2767952370 on OpenAlexafffund
Ramanpreet Annie Bahra

Bibliographic record

VenueFat Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsYork University
FundersYork UniversityWilfrid Laurier University
KeywordsSociologyHappinessNormativeDeviance (statistics)Materiality (auditing)TemporalityMaterialismAestheticsGender studiesPrivilege (computing)PoliticsRacializationEpistemologyHumanismSocial psychologyRace (biology)PsychologyLawPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Within the expanding field of fat studies, we have come to understand different ways that fat bodies have been scrutinized for their “deviance” from the anticipated norms of society. The author interrogates the role racializing assemblages play in categorizing and disciplining bodies based on humanism and its fixed sizeist-normative temporality. With the supported idealized form, the human template as well as racialized and fat bodies are distinguished as lacking under the criteria of deficit. Subsequently, the promise of happiness in the context of Whiteness and thinness is set up as a form of normative time that must be followed to gain access to the privilege of being human. An autoethnographic methodology is used to situate the author’s own experience as a fat, racialized woman grounded in theory. In the latter portion of the paper the author discusses the affirmation of the body and its materiality within a new materialist framework. What is of utmost importance to consider in affirmative politics is that one’s race and fatness can be accentuated and accepted as forms of difference informing and intermingling with each other and our surroundings as they are brought to the forefront.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.527
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0150.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.171
GPT teacher head0.492
Teacher spread0.321 · 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 designObservational
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
Published2017
Admission routes2
Has abstractyes

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