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Record W4394812466 · doi:10.1080/21604851.2024.2340012

Compassionately fat: An autotheoretical exploration of queer bodies

2024· article· en· W4394812466 on OpenAlexaff
Phillip Joy, Adam Davies

Bibliographic record

VenueFat Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsUniversity of GuelphMount Saint Vincent University
Fundersnot available
KeywordsQueerSociologyPrivilege (computing)Gender studiesHeteronormativityCompassionScholarshipQueer theoryAestheticsNarrativeStorytellingContext (archaeology)PoliticsPolitical scienceLawLiteratureArt

Abstract

fetched live from OpenAlex

The concept that queer scholars hold a shared responsibility for all queer people forms the driving inspiration for this exploration at the juncture of queerness and fatness. We advocate that cultivating compassion can be a way for queer fat people to engage in new relations with their embodiment that reflect the tenets of fat social justice. Drawing from fat activists of color and fat studies scholars, our relationships with our bodies are political. Within queer men’s communities, hegemonic constructs of queer men’s bodies privilege muscular bodies that are lean with very little fat. In this article, we advocate for compassion as an approach to queer fat activism. Compassion is a social construction and social beliefs, values, and knowledge shape not only how compassion is practiced and enacted but also who is able to receive compassion. We cannot ignore how cis-heteronormativity and fatphobia influences the way compassion is understood and practiced. In this dual autotheoretical work, we will explore the meanings of compassion within the context of fat activism as queer men. Our approach combines practices of self-narration with critical theory, and thus unites the authors’ bodily experiences through creative means with philosophy. We relate, through storytelling, our experiences of queerness and (non)compassion with fat studies scholarship for the purpose of advocacy and social transformation.

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 categoriesnone
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 score0.459
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.138
GPT teacher head0.405
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes1
Has abstractyes

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