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War on Weight: Capturing the Complexities of Weight with Hermeneutics

2022· article· en· W4402507984 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Applied Hermeneutics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Calgary
FundersKenyon College
KeywordsMetaphorNegotiationPoliticsPhenomenonConstruct (python library)SociologyPolitical sciencePsychologySocial psychologyGender studiesSocial scienceEpistemologyLaw

Abstract

fetched live from OpenAlex

Purpose: In professional practice, body weight issues are typically considered from an individual-level standpoint. In contrast to this dominant perspective, we highlight that body weight has prominent social, economic, and political influences and connotations. An examination of the social complexity of weight provides opportunity to shift focus from individual to societal and structural influences on perceptions of weight. Methods: Seven renowned experts in weight-related issues with at least 10-years-experience in various fields from across Europe, Australia, the United States, and Canada participated in interviews about their professional experience with weight. Interviews were analyzed using hermeneutic methods via an iterative interpretive process. Results: The interviews revealed a battlefield, a war waged on weight. War emerged as an overall metaphor that included aspects of: war on obesity, bodies as battlefields, war camps, war fronts, entrenchment and negotiation and, finally, the phenomenon of “no man’s land.” Conclusions: In many ways, language itself limits us from capturing the complexities of weight. The war metaphor provides a way of understanding the intensity of the firestorm surrounding the construct of weight. New understandings from what we might refer to as veterans of the war on weight offer hope for transformation, not just win or lose, but a hermeneutic wager of possibility.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.056
GPT teacher head0.362
Teacher spread0.306 · 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