An Evaluation of the Effect of Child‐Directed Television Food Advertising Regulation in the United Kingdom
Bibliographic record
Abstract
Faced with increasing rates of childhood obesity, the U.K. government has recently introduced stricter regulations to reduce children's exposure to advertising of foods high in fat, sugar, and salt (HFSS). The purpose of our paper is to quantify the impact of HFSS regulations on household expenditures in the three sequential “phases” that reflect the evolution of the U.K. regulatory regime: a period of no regulation, a period of voluntary self‐regulation at company level, and the current coregulation implemented by the industry code of practice. Results suggest that coregulation is the only regulatory mechanism that leads to a significant reduction in advertising expenditures. The reduction in total advertising of £11.4 million is composed of an £15.2 million decrease in television (TV) HFSS advertising expenditures. This decline in TV advertising is partially compensated by other media, which translates to a change in household HFSS food and drink expenditures. However, self‐regulation and coregulation lead to reduction on HFSS expenditure. As a result of regulation, households without children decrease HFSS drink expenditures by £5.2per capita(per quarter), while households with children decreaseper capitaHFSS food expenditures by £14.9 and HFSS drink expenditures by £5.6 (per quarter).
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".