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Record W4294584539 · doi:10.1080/08870446.2022.2117810

The optics of weight: expert perspectives from the panopticon and synopticon

2022· article· en· W4294584539 on OpenAlexafffund
Shelly Russell‐Mayhew, Andrew Estefan, Nancy J. Moules, Danielle Lefebvre, Janelle M. Morhun, Jessica F. Saunders, Katherine Wong, Maxine Myre

Bibliographic record

VenuePsychology and Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsDietingPsychologyNormativePanopticonMetaphorInterpretation (philosophy)Social psychologyEpistemologySociologyMedicineWeight loss

Abstract

fetched live from OpenAlex

That we all weigh something is a fact of life, yet the material reality of weight is refracted through multiple layers of surveillance revealing contradictions in experience and understanding, depending on one's vantage point. We explored the complexities of weight with the specific aim of furthering understanding of this multifaceted surveillance. We used hermeneutics, the philosophy and practice of interpretation, as the method of inquiry. Ten experts by experience and seven professional experts participated in interviews, which were audio- recorded, transcribed, and analyzed. Interpretations were developed through group discussions among the eight authors and reiterative writing. Using the metaphor of optics, we demonstrate how the interplay of the panopticon (the few watching the many) and synopticon (the many watching the few) help us gain a deeper understanding of weight through "fitting in," being "captured by numbers," "dieting: the tyrannic tower," and "the male gaze." Monitoring and judging body weight have become so normative in Western society that "weight watching" practices are synonymous with good citizenship and moral character. This study offers insight about how weight is conceptualized in personal and professional contexts, with implications for body image, dieting, eating disorders, public health, and weight bias.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
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 head0.115
GPT teacher head0.510
Teacher spread0.394 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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