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Enregistrement W4392094819 · doi:10.1111/amet.13263

Fat in four cultures—a global ethnography of weight By CindiSturtzSreetharan, AlexandraBrewis, JessicaHardin, SarahTrainer, and AmberWutich. Toronto: University of Toronto Press, 2021. 236 pp.

2024· article· en· W4392094819 sur OpenAlexaboutno aff
Fernanda Baeza Scagliusi

Notice bibliographique

RevueAmerican Ethnologist · 2024
Typearticle
Langueen
DomaineHealth Professions
ThématiqueObesity and Health Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEthnographySociologyAnthropologyMedia studies

Résumé

récupéré en direct d'OpenAlex

Before reviewing Fat in Four Cultures—a Global Ethnography of Weight, I would like to explain my positionality. I am a Brazilian professor and researcher on weight stigma and body image. As such, I found it refreshing to read a book concerning fatness that does not center around data from only European countries or the United States. Brazil produces a reasonable body of work regarding body image and weight stigma, one that generally does not appear in systematic reviews and consensuses. These types of publications, however, are highly read and cited, and they often provide the basis for worldwide policy making. I also would like to acknowledge that I am a white, upper-middle-class, heterosexual, and cisgender woman, without disabilities, and whose body mass index would be classified as “normal” by health organizations. Regardless, I possess something that Brazilians generally loathe: a considerable amount of fat in my belly. This has led to aesthetic pressure to be thin and toned (but never the processes of discrimination and exclusion to which fat people are subject in Brazil). Owing to my position, I could say a “thing or two” about fatness in my country. It is admirable how the authors of this book provide such an intricate analysis in places far from their origins and in the United States. SturtzSreetharan and coauthors conducted a global ethnography of fatness that was instigated by what they had already observed in their respective fieldwork: not only have food systems and lifestyles been globalized but also the meanings attributed to fatness. Each author had several years (even decades) of experience researching fatness and stigma in different locations. Although they could have brought together and compared the evidence that each has published over the years for a formidable book, the high standards of the authors instead led to a carefully designed comparative, cross-cultural ethnography. This allowed them to take on two challenging tasks: to take a systematic approach that would make comparisons possible, with the same protocols and methods for each site, and to attend to the historical, political, sociocultural, and economic context of each place. The following field sites were selected based on the authors’ previous experience and these places’ different rates of obesity and presence of weight stigma: (1) the suburban and peri-urban area around Osaka, Japan; (2) the peri-urban and rural area north of the state of Georgia, United States; (3) the small city of Encarnación, Paraguay; and (4) the capital city of Apia, Samoa. The book is organized in eight chapters. The first is an introduction, mainly based on ethnographic studies. The second chapter describes the study methods and is supported by several appendixes that present the full protocol used, demonstrating the five researchers’ positionalities and ethnographic approaches, and the procedures used to collect, manage, and analyze data. The following four chapters describe and interpret the data collected with adult women and men at each field site and present, from an emic perspective, the reasons why people get fat, when fat is considered bad, and who gets fat. They also discuss the context of each site and reflect on the experience of interviewing (and often eating together with) the participants. The seventh chapter discusses the shared beliefs about fatness (and the subtle differences) across the four sites, and the last chapter concludes the book. Appendixes follow the conclusion, providing methodological details and recommendations for several audiences. The book's main argument is that people from such different sites understand that being fat is a matter of personal responsibility, even if they acknowledge that structural factors, such as foodways, poverty, and changes in societies’ traditions, contribute to weight gain. The authors, however, found that this is a fluid social construction. In Japan, while businessmen are less stigmatized when they gain weight, owing to their job demands, the fault is transferred to their wives, who were viewed as unable to play their hegemonic female role as family caretakers. In Samoa and Paraguay, responsibility for gaining weight lies somewhere “in between” individuals, communities, and families. Thus, the “fat is your own fault” mantra was globalized but not incorporated in the “pure” neoliberal meaning dominant in the United States. All in all, this book is both a masterpiece and a masterclass. The authors seem to find themselves at several crossroads. There is a need not only for systematic comparison and standardized methods but also for context and for incorporating the knowledge that these authors have built over the years at each site. The original language of the data in each field is crucial for understanding the topic; however, there is a need for transcription and translation. The authors are devoted and well-respected anthropologists who also want to reach policy makers, scholars, students, health professionals, and activists to promote a change in global health. The book not only communicates research but also serves as a tool for teaching and learning. The authors take the right direction at every crossroad, and they tell you how they did it. Although the authors followed the canons of anthropology and its core—the ethnographic work—they did it through collaboration. And that is the book's breakthrough. The authors clearly state that their writing is a feminist practice of collaboration, and the reader can see it multiple times throughout the book. The processes of theme identification, metathematic analysis, and cross-cultural comparisons, detailed in appendix B, are clear examples of this. The authors open a path for understating fatness and stigma worldwide, which is very exciting for a Brazilian researcher like me. I can confidently state that there are profound researchers from the Global South who study weight stigma and who should be “brought to the table” with the same level of appreciation as those from Global North. Their work should not be considered of national interest only. Considering my white, upper-middle-class, able, heterosexual, cisgender, and thin privileges (which, in general, apply to the authors), our next challenge around the globe is to embody a deep intersectional perspective rooted in the voices of those who can stand for and demand their needs: fat people, especially those who are marginalized and seen as “noncompliant.” To build such a path, this book should be of great interest to medical, cultural, and biological anthropologists and to those who work in the field of critical studies of class, race, and gender. This book could be delightful reading for the most rigorous methodologist and the beginning ethnographer. The book's compelling writing style also favors the audience that I believe needs it the most: health professionals and policy makers. The dangerous consequences of actions by health professionals and policy makers on global health were researched by Alexandra Brewis and Amber Wutich throughout their entire careers, and they are made more than crystal clear in this book, which is the fruit of a collaboration among five bright and brave women.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,470
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,002
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,387
Écart entre enseignants0,358 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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