Reality Television and the Promotion of Weight Loss: A Canadian Case
Bibliographic record
Abstract
One of the most popular themes associated with reality television is the ‘make-over show’, and its usefulness for advertising is evident; it not only promotes the ideology of beauty and thinness, but also of consumption. Scholar Eileen Saunders sums up the link between ide-ologies of beauty and consumption quite concisely: “in order to motivate consumers to buy beauty products, there needs to be some assurance of transformation offered” (2008:114). More specifically, the bulk of make-over based reality programming has shifted to achieving weight-loss goals that reflect the beauty signifier of thinness. With the so-called ‘obesity epidemic’ affecting Americans across the country, programs such as The Biggest Loser claim to promote a ‘healthy lifestyle’ that will help participants obtain a beautiful body. Unfortunately, this healthy lifestyle is really only an extreme and temporary ‘quick fix’ to a serious problem. Research done on the implications of consumerism that this particular program can have on the audience concludes that, most notably, it promotes the consumption of certain products in order to achieve and maintain a ‘healthy’ (or socially acceptable) weight. What has not been researched, however, is the extent to which the same notions appear on Canadian television.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".