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Record W2615407599 · doi:10.1080/09518398.2021.1930253

‘One-size-fits-none’: a situational analysis of weight-related issues in schools

2021· article· en· W2615407599 on OpenAlexaffabout
Alana Ireland, Shelly Russell‐Mayhew, Dan Wulff, Tom Strong

Bibliographic record

VenueInternational Journal of Qualitative Studies in Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of CalgarySt. Mary's University
Fundersnot available
KeywordsSituational ethicsExtant taxonSituation analysisCurriculumPsychologyFocus groupPublic relationsSociologyPedagogySocial psychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Many researchers have explored the impact or effectiveness of eating disorder (ED) and obesity (OB) prevention in schools. Few, however, have investigated integrated prevention, and despite recommendations to shift the individual focus of prevention to environmental or systemic change, even fewer researchers have considered the broader situation. In this study, we explored how weight-related issues are negotiated in Canadian schools, and what institutional and social practices influence their construction. Situational analysis (SA) was used to develop a broad picture of the complexities of the situation and the differences or tensions extant. Data such as research literature, participant interviews, and educational curricula/policy documents contributed to a comprehensive understanding of the situation. Mapping processes indicated multiple tensions and highlighted the importance of opening up conversations. Findings emphasized the importance of exploring ways to (a) promote acceptance of all bodies and (b) change policies or practices that contribute to the stigmatization of individuals based on body size.

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)0.005
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.280
GPT teacher head0.657
Teacher spread0.376 · 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 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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