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Record W7028500013

Fatphobia as Marginalization: The Impacts on Women in the Public Sphere

2021· other· en· W7028500013 on OpenAlexaff

Bibliographic record

VenueYork University Digital Library (York University) · 2021
Typeother
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsYork University
Fundersnot available
KeywordsIdeologyPerspective (graphical)IntersectionalityRace (biology)Work (physics)RacismEthnic groupHabitus
DOInot available

Abstract

fetched live from OpenAlex

This paper seeks to explore the impact that fatphobia has on Western society, specifically the female body. Using existing literature, this research aims to deepen the knowledge and experience around fatphobia and its pervasiveness in the public sphere. Key questions explored are centered around how fatphobia impacts women in Western society, how fatphobia is created and maintained, and the exploration of where fatphobia is most pervasive in a person’s life. The study will analyze its research findings through a feminist and intersectional theoretical perspective. Some of the key findings in this study were that fatphobia is largely connected to patriarchy, Western culture, and colonization. As well as classism and neoliberalist ideologies and how these ideologies create and maintain fatphobic beliefs. The intersection between fatphobia and race was explored, however, there was a significant lack of perspective in the literature from fat women of colour. 
\nAdditionally, analysis on the biomedical discourse around obesity and weight discrimination was explored, eliciting extreme discrimination against fat bodies. Based on this information, it is apparent that awareness of fatphobia needs to be explored further in professional research and in day-to-day life. Specifically, recognizing fatphobia as a form of marginalization is recommended to be included in social work education and implemented into social work practice to ensure more inclusive knowledge and practice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.000

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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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