A Reflection of Reality? The Consumption and Reproduction of Obesity Discourses by The Biggest Loser’s Viewers Through Facebook
Bibliographic record
Abstract
The Biggest Loser promotes itself as an avenue for ‘obese’ individuals to lose weight through exercise and diet modification with the end goal of a ‘healthy lifestyle’ (NBC, 2013). A driving premise behind The Biggest Loser is the idea that Western countries are in the midst of an ‘obesity epidemic’ and immediate action by all citizens is required. The purpose of this study was to provide insights into viewers’ consumption of the obesity discourses reproduced by The Biggest Loser, through the social media platform Facebook. Viewers’ Facebook posts were analyzed and categorized under the theoretical framework of biopedagogy. Data analysis observed that viewers’ Facebook posts reproduced obesity discourses concerning children, active or inactive citizens and inequalities. Facebook enables active participation of body surveillance and also serves as a multiplier of surveillance. The authors maintain that social media is a form of cultural texts; these texts reflect broader cultural understandings. Although some scholars argue that social media can facilitate movements of resistance, the authors observed that The Biggest Loser viewers reproduced obesity discourses.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".