Eat This! How Fast-Food Marketing Gets You to Buy Junk (and How to Fight Back) by A. Curtis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».