The Disruptive Screen: Understanding the Multiple Lifestyle Risks Associated with Heavy TV Viewing
Bibliographic record
Abstract
In the previous chapter I argued that over the last 25 years social scientists have provided strong evidence of the limited health risks associated with exposure to branded advertising. The methods used have included both experimental and survey studies. The largest effects have been noted in experimental studies where exposure to advertising is carefully controlled. Weaker effects have been noted in cross-sectional and longitudinal field studies where exposure to advertising is measured by total TV viewing. The evidence of marketing risks has been based largely on evidence of a statistical relationship between two measurable variables in the US – TV viewing time and BMI. This relationship was examined across a number of studies. Reviews of this literature suggested that exposure to TV food advertising targeting children makes a small but consistent contribution to their weight gain by influencing their brand preferences and requests to parents. But as critics have stated, there are three limitations in this literature: the influence of TV advertising on diet is small, the effects can be mitigated by good parenting and TV advertising is not the only reason why heavy viewers gain weight. In short there are many other factors in children’s lives that can moderate advertising’s impact on children’s weight gain including their parents’ unwillingness to purchase what they ask for as well as their own regular participation in active leisure. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".