The optics of weight: expert perspectives from the panopticon and synopticon
Bibliographic record
Abstract
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.
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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 | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".