Exploring Media Representations of Weight-Loss Surgery
Bibliographic record
Abstract
Scholars have problematized popular culture and media (re)presentations of obesity/overweight. However, few have considered the ways bariatric surgery, a rapidly growing treatment for morbid obesity, fits within the discussion. In this article, we explore news media (re)presentations of bariatric surgery using an eclectic approach to critical discourse analysis. Our findings reveal dominant discourses about bariatric surgery and the surgical population, providing an understanding of media (re)presentations as possible contributors to bias, stigmatization, and discrimination. Novel in our findings was our identification of subject positions in the dominant discourses (which were biomedical and benevolent government). We argue that existing (re)presentations of bariatric surgery are highly problematic because they reinforce oversimplistic and binary understandings of weight-loss surgery and obesity, weaving a highly gendered fairy-tale narrative and ultimately promoting weight-based stigmatization.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".