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Record W2008042834 · doi:10.1080/21604851.2014.927209

Mother Blame, Fat Shame, and Moral Panic: “Obesity” and Child Welfare

2014· article· en· W2008042834 on OpenAlexaffabout
May Friedman

Bibliographic record

VenueFat Studies · 2014
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsShameBlameMoral panicPsychologySocial psychologyWelfareObesityPanicDevelopmental psychologyCriminologyPsychiatryPolitical scienceAnxietyMedicineEndocrinologyLaw

Abstract

fetched live from OpenAlex

AbstractThis article seeks to examine the scholarly journal articles and print media that describe the intersections of child protection and pediatric "obesity," arguing that presentations of these cases and related interventions rest at the nexus of dominant discourses of child-centered parenting and fears of the "obesity epidemic." The messaging in both scholarly literature and news media on this topic echoes taken-for-granted truths about fatness and parenting and applies them to the contentious terrain of child welfare. In so doing, this literature reifies myths of both poor parenting and bodily failures and inscribes these failures on the bodies of the children described therein.KEYWORDS: child welfarediscourse analysismother blame"obesity epidemic" Notes1. "Obesity" and "morbid obesity" are not always qualified in these sources, but are sometimes characterized as body mass index over the 99th percentile.2. Scholarly articles were sought through sociology, social sciences, and social work databases, as well as legal databases. Google Scholar was also used. Search terms for both web searches (for news accounts) and scholarly searches included the words "obesity," "child protection," "child welfare," "fat," and "foster care."Additional informationNotes on contributorsMay FriedmanMay Friedman teaches in the School of Social Work at Ryerson University where she studies mothers, fat, transnationalism, and reality TV.

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)0.000
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 score0.384
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
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 head0.076
GPT teacher head0.424
Teacher spread0.348 · 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

Citations66
Published2014
Admission routes2
Has abstractyes

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