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Record W4408064639 · doi:10.1080/21604851.2025.2469357

Fatphobia as a form of gender-based violence: Fat women, public space and body belonging work

2025· article· en· W4408064639 on OpenAlexfundno aff
Elizabeth Mohr, Kimberly Jamie, Hester Hockin‐Boyers

Bibliographic record

VenueFat Studies · 2025
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
FundersLaidlaw Foundation
KeywordsGender studiesSpace (punctuation)Work (physics)SociologyPublic spacePsychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

In this article, we propose bringing together theoretical frameworks from fat studies and research into street harassment, as a form of gendered violence, to provide a novel lens for thinking about fat women’s experiences of public space. By focusing on the gendered politics of public space itself, we show how fears of fat-based and gender-based street harassment and abuse work together to create a complex sense of “non-belonging” for fat women. Coupled with primary interview data gathered from twenty-one self-defined fat women, our approach brings together theoretical frameworks from fat studies and research into street harassment to provide a novel lens for thinking about fat women’s experiences of public space. Specifically, we identify and explore points of confluence where experiences of fatphobia and street harassment mirror each other – exclusion from public space, intrusion as a means of policing non-belonging bodies, and what we call body belonging work as an active process of accomplishing belonging. We suggest that current policy attention to gender-based violence represents a timely moment to address the intersectional nature of women’s experiences.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.097
GPT teacher head0.457
Teacher spread0.360 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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