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Record W4254963960 · doi:10.32920/ryerson.14663958

Resisting Medical Discourses in Fat Social Work Practice

2021· preprint· en· W4254963960 on OpenAlexaff
Taylor Ardel Thornton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsResistance (ecology)ConformitySocial workPublic relationsSociologyEthnographyWork (physics)Power (physics)Service (business)Social psychologyPsychologyPolitical scienceBusinessMarketingEngineering

Abstract

fetched live from OpenAlex

This Major Research Paper conducted an institutional ethnography of social work practice with fat service-users in medical settings, exploring the resistance or conformity taken in clinical settings to medical discourses on fatness. Using a voice-centered relational method, three social workers were interviewed on their experiences working with fat- identified clients within medical settings. The interviews explored the role of social work in medical settings, the operation of power structures and cultural discourses that restrict or limit social workers’ capacity for engagement from social perspectives, and the resistance practices workers use to navigate their practices to maintain anti-oppressive social work practice. It was found that there are significant issues with the medical model’s engagement with fat service-users and that, while there are significant barriers to fat positive social work practice, it is through the use of language, client- centeredness, teaching moments, and advocacy, that anti-oppressive social workers navigate these spaces.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0190.080
Scholarly communication0.0130.007
Open science0.0020.020
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.156
GPT teacher head0.560
Teacher spread0.404 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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