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Record W3209745154 · doi:10.32920/ryerson.14664243.v1

Fat chat : an exploration of obesity discourses in Canadian media and their impacts on social work

2021· preprint· en· W3209745154 on OpenAlexaffabout
Samantha A. Abel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsBlamePower (physics)Work (physics)ObesitySociologyCritical discourse analysisDiscourse analysisPublic relationsGender studiesSocial psychologyPolitical sciencePsychologyMedicinePoliticsEngineeringLaw

Abstract

fetched live from OpenAlex

This Major Research Paper conducted a critical discourse analysis of Canadian Press articles focused on obesity. This research sought to understand how the articles constructed obesity, what discourses were operating, and what power relations were at play. The three main discourses that shaped the articles were mother blame, the medical model, and economics. They became evident through photographs, language used, gendered power relations, medicalized understandings of health and solutions to obesity, and who was profiting or benefitting from these understandings and solutions. Social work practitioners and educators need to consider these discourses when conceptualizing obesity, and strive to contextualize individual experiences of fatness within broader structural and systemic power relations. Social workers also need to be cautious about reproducing oppressive anti-obesity practices, social work is a profession that has historically been an agent of social control and discipline.

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)0.008
metaresearch head score (Gemma)0.021
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 score0.095
Threshold uncertainty score0.691

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0150.017
Science and technology studies0.0410.022
Scholarly communication0.0170.006
Open science0.0020.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.000

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 head0.215
GPT teacher head0.475
Teacher spread0.260 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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