The politics and practices of representing bodies in capitalism
Bibliographic record
Abstract
This publication was produced from a roundtable discussion about the cultural politics of representation and metabolic illness commissioned by the organizers of the 2021 “Just Food” conference. The immediate occasion for the roundtable was a film that had been submitted to the conference titled, El Susto: Mexico’s Love Affair with Sugary Drinks Turns Deadly, produced by Karen Akins. Just Food conference organizers wished to screen the film and provide an academic roundtable discussion to unpack some of the tropes that frequently surround discussions of globalized markets of processed foods and the rise of metabolic illness (see also, for example, the NYTimes multi-part series on the rise of “globesity”). With questions posed by the Chair, Emily Yates-Doerr, the roundtable discussion focused broadly on the challenge of problematizing the inequitable burden of metabolic illness among equity-seeking groups without stigmatizing those who consume foods that are often seen as the cause of disease. More specifically, panelists considered how to redress the corporate greed that drives expanding global markets for processed food, without pathologizing racialized and fat bodies. We jointly insisted that academic inquiry and activism should keep the focus on political and economic structures, such as trade agreements and health policies, that undergird health inequities, rather than individual dietary choice or a lack of education. The roundtable panelists were selected by the Just Food conference organizers because of their expertise on metabolic disorder, the cultural practice of capitalism(s), and techniques of representation. Because we believe the conversation has relevance for anyone working on or thinking about health and its representation – in or outside of Mexico -- we are reproducing it here. We have lightly edited the transcript of the conversation to help with readability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.115 |
| Scholarly communication | 0.033 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".