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.
Bibliographic record
Abstract
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
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| 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".