Eat This! How Fast-Food Marketing Gets You to Buy Junk (and How to Fight Back) by A. Curtis
Bibliographic record
Abstract
Curtis, Andrea. Eat This! How Fast-Food Marketing Gets You to Buy Junk (and How to Fight Back). Illustrated by Peggy Collins. Red Deer Press, 2018. Andrea Curtis’s first children’s book was What’s for Lunch? What school children Eat around the World, and her latest book Eat This!: How fast Food marketing gets you to buy junk (and how to fight back) is written for the modern family. It talks about product placement, ads on the internet, the all-natural myth of orange juice and more. Even though this book is word-heavy (there is a glossary) there are bright colourful pictures, by Peggy Collins, accompanying almost every page. However, they cannot show the advertising of the actual products they want to talk about. So a box of frosted flakes becomes sugar rings with a tiger mascot, and any clown can represent McDonald's. Intermittently, it has real-world examples of people fighting fast-food marketing around the world. For example, the Game Changer campaign in Australia, which focuses on the ads in cricket for junk food, alcohol, and gambling. At the end of the book, there is a list of things to try to challenge fast food and marketing strategies. Their goal is to get the reader engaged with what they have just read, offering examples such as potlucks that celebrate diversity, or observing your favourite show for product placement. There are also multiple facts sprinkled into the book like how part of Philadelphia's soda tax is used for improving parks, or how Peru has banned junk food in schools. Overall the book discusses an important topic that is all too relevant in the age of the internet. Better yet, its goal is getting children to engage with advertising in a critical way. Children will benefit from the book, as it explains how advertisers don’t always have our best interests at heart and can help open a dialogue with adults on the subject. Highly recommended: 4 out of 4 stars Reviewer: Kaia MacLeod Kaia MacLeod, a member of the James Smith Cree Nation, is an MLIS candidate at the University of Alberta. Her bachelor’s degree was in Film Studies, which she sometimes likes to call a degree in “movie watching,” she enjoys exploring how folklore is represented on film and in online content.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.275 | 0.265 |
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".