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Record W2561032386 · doi:10.1111/1467-9566.12537

Obesity, bodily change and health identities: a qualitative study of Canadian women

2016· article· en· W2561032386 on OpenAlexfundaboutno aff
Andrea E. Bombak, Lee F. Monaghan

Bibliographic record

VenueSociology of Health & Illness · 2016
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCenters for Disease Control and PreventionManitoba Health Research Council
KeywordsReflexivityWeight stigmaWeight lossQualitative researchStigma (botany)NarrativeDistressObesityGender studiesPsychologySocial psychologyEveryday lifeSociologyGerontologyMedicineClinical psychologyOverweightPolitical scienceSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

Medicalised concerns about an obesity crisis persist yet more needs to be learnt about everyday orientations to weight (loss). This article reports and analyses data generated using qualitative methods, including repeated interviews and fieldwork conducted over one year in Canada with women (n = 13) identifying as (formerly) obese. Three ideal types are explored using empirical data: (1) hopeful narratives; (2) disordered eating distress; and (3) weight-cycling or stagnation. Core themes include women's desire to embody a thin(ner) future and the good life, the harms of intentional weight-loss, and resignation to living as a fat woman whilst nonetheless challenging stigma. The article contributes to critical studies of weight/fatness, the sociology of bodily change and the embodiment of health identities. In concluding, we call for reflexive change in bodies of health knowledge, policy 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 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)0.009
metaresearch head score (Gemma)0.016
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 score0.091
Threshold uncertainty score0.469

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0380.022
Scholarly communication0.0070.003
Open science0.0030.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.246
GPT teacher head0.518
Teacher spread0.273 · 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

Citations29
Published2016
Admission routes2
Has abstractyes

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