War on Weight: Capturing the Complexities of Weight with Hermeneutics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".