Fat in Four Cultures: A Global Ethnography of Weight By CindiStrutz Sreetharan, AlexandraBrewis, JessicaHardin, SarahTrainer, and AmberWutich. Toronto, Canada: University of Toronto Press. 2021. pp. 222.
Notice bibliographique
Résumé
In Fat in Four Cultures: A Global Ethnography of Weight, five anthropologists endeavor to provide a cross-cultural, comparative, and collaborative analysis of how fatness is framed and experienced around the world.The volume is part of the University of Toronto Press's Teaching Culture: Ethnographies for the Classroom series, which is specifically aimed at using "urgent issues faced by people around the globe today" (About the Series n.d.) to introduce undergraduate students to the methods and theoretical frameworks that guide ethnographic research.The "urgent issue" at stake in Fat in Four Cultures is not totally clear, as Cindi Strutz Sreetharan, Alexandra Brewis, Jessica Hardin, Sarah Trainer, and Amber Wutich rely on the connection between fatness and disease in articulating their rallying cry for structurally-focused obesity interventions and simultaneously draw from fat studies to question the widespread vilification of largeness.In addition to clarity of issue, given the aims of the Teaching Culture series, a reader might expect to find in Fat in Four Cultures accessible language, compelling case studies, impeccable methodologies, and careful analysis.While the book is an easy read (a slim volume and adequate for introducing undergraduate students to cross-cultural difference in medicine), and it is also an impressive example of transparency in (collaborative) protocols for data collection and analysis, the book falls short of expectations.Fat in Four Cultures is made up of eight chapters and five appendices.The heart of the book is four case studies focused on Osaka, Japan (chapter 3), the state of Georgia in the United States (chapter 4), the town of Encarnación in Paraguay (chapter 5), and the capital city of Apia in Samoa (chapter 6).For a volume purporting to offer a "global ethnography," perspectives drawn from Europe, the Middle East, South Asia, the whole of Africa, the Caribbean, and Central America are notably absent.The authors of the volume-five cisgender white US-passport holding women who "do not consistently identify as fat" (34)-have prioritized and implemented a single research protocol to identify common "meta-themes" across field sites.In this way, the authors envision the volume as following in the footsteps of other multi-sited studies like Brigitte Jordan's Birth in Four Cultures (1978) and other collaborative projects like the Six Cultures Study (see LeVine 2010).The authors take their team-based approach seriously, to the point where the data-centered chapters are written in the third person (for example, instead of "I took the train" it would read "Cindi took the train") to reflect the collective processes of research development, analysis, and writing.In short form, the authors of Fat in Four Cultures argue that medicalization of largeness and individualization of responsibility for weight management (both associated with the Global North) have been "transmitted around the world" (142).In other words, where bodies, food, and eating may have had
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,008 | 0,009 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».