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Record W2419013933 · doi:10.1177/0896920516652456

Race, Online Space and the Feminine: Unmapping ‘Black Girl Thinspiration’

2016· article· en· W2419013933 on OpenAlexaff
Nicole Schott

Bibliographic record

VenueCritical Sociology · 2016
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociologyGender studiesGirlRacismPower (physics)White (mutation)Race (biology)White privilegeIdeal (ethics)HegemonyPostmodernismAestheticsPrivilege (computing)Movie theaterPoliticsPsychologyArtLawPolitical scienceVisual artsLiterature

Abstract

fetched live from OpenAlex

A major component of the online pro-eating disorder culture (‘pro-ana’ and ‘pro-mia’) is what is referred to as ‘thinspiration’ or ‘thinspo’, which consists of images, slogans and videos aimed at inspiring the pursuit of extreme thinness. More recently, there is a specific kind of thinspiration, labeled online as ‘black girl thinspiration’, that seeks to inspire black women to reject fuller-figured body shapes as beautiful and responsible. Through the application of a spatial analysis, I contend that pro-eating disorder environments are spaces where women attempt to de-mark their racialized bodies through hard work, will-power and mastery over their desires. Theorizing from critical race and feminist postmodern perspectives, this article disrupts the white hegemony and privilege of the thin ideal. This disruption is achieved through unmapping how modern capitalism, sexism, and racism operate in unison to produce women who starve, purge, abuse laxatives and hate their bodies, while highlighting the tremendous violence embedded in these practices.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score1.000

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 studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.361
Teacher spread0.328 · 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 designTheoretical or conceptual
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

Citations9
Published2016
Admission routes1
Has abstractyes

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