Bibliographic record
Abstract
In this article, we argue that mass media representations of obesity operate biopedagogically and, in doing so, teach viewers how to think and feel about what it means to be fat. These teachings are what we call “life lessons”—that is, lessons aimed at instructing viewers in how to relate to both their own bodies and the bodies of others—and are readily discernible in a wide variety of magazines, radio segments, and television shows. We suggest that life lessons of this sort are neither separate nor entirely distinct from public health campaigns, but rather overlap with and, at times, intensify their messages. With this in view, we undertake a four-part analysis of how the North American mass media represent obesity. In the first part, we outline contemporary theories of biopower, biocitizenship, and biopedagogy and how they can be used to make sense of these representations. In the second part, we contextualize them by providing an overview of relevant public health policy such as the United State’s Let’s Move campaign and the World Health Organization’s Prioritizing Areas for Action in the Field of Population-Based Prevention of Childhood Obesity. In the third part, we present a systematic review of the critical literature on representations of obesity with an emphasis on the North American media landscape. And in the fourth part, we submit an episode of the popular television program Nip/Tuck to an in-depth critical examination as a case study of how mass media representations of obesity function biopedagogically. In the end, we show that the representations we discuss not only reflect public health priorities but also reproduce neoliberal ideas about how to manage the life of the human body.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.121 | 0.034 |
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".