Bibliographic record
Abstract
It is not a secret that the United States is experiencing a moment of deep political and cultural polarization. The lines are typically drawn in clear, stark terms: blue versus red, left versus right, and Democrat versus Republican. These dividing lines can take on a food dimension: meateating conservative versus latte-sipping liberal, beer versus wine, hunter versus tofu-lover. In A Decent Meal, Michael Carolan uses food to unpack key dimensions of political polarization in the United States and to explore how food can build embodied bridges of emotional connection, understanding, and empathy. It is not so much a book about food itself, but a book about how food can facilitate encounters that open hearts and minds. Carolan reports on the results of various formal and informal experiments that use embodied food experiences. Each experiment is designed to investigate how experiencing food and food work can leave people more open to new ideas and perspectives. In one experiment, Carolan rents out a strawberry U-pick farm and invites thirty-one “pro-wall, anti-immigration” participants to spend a day doing backbreaking labor in hot conditions. American strawberries are typically picked by low-wage workers, often undocumented immigrants, and often in harsh working conditions involving myriad chemicals. Carolan’s participants signed up to experience a day designed to replicate this labor. They were invited to take pictures documenting their day; these photos shifted from fun selfies and picturesque landscapes in the morning to depictions of dirt, sweat, and exertion by the afternoon. The hot, claustrophobic, tiring experience of picking strawberries had a powerful impact on participants, who were interviewed before and after the day of picking. In general terms, participants were more open to learning about the conditions of strawberry production after they had experienced it themselves and were less likely to have a hostile view toward immigrants who were “taking their jobs.” Using food experiences to build bridges of empathy is not a strategy that Carolan limits to rightwing nativist perspectives. Carolan also employs innovative methodological techniques to bring foodies and left-leaning urban agrarians into the American heartland. Here, lefty-liberals and urban food activists meet farmers who grow monocrops of wheat and soy, drive trucks, and do not see the term “gun nut” as an insult. Carolan identifies the lack of empathy directed toward rural people and American farmers. In a deeply ironic twist on alternative food politics, urban food activists can sometimes have little sympathy for conventional farmers. This deficiency often goes unnoticed—an absence that is in part due to a critical mindset that equates farmers and rural residents with xenophobic, white supremacist perspectives. Carolan does not flinch from the racist, reductionist ideas he finds in rural America. He baldly describes these perspectives and outlines his own discomfort talking to some interviewees. At the same time, Carolan shows the limitations of a reductionist, stereotypical vision of rural people and farmers which closes off the understanding of rural struggles—the struggle to keep farms financially solvent, access health care, preserve mental health, and maintain community as young people abandon the countryside. Carolan’s data show how left-leaning urbanites can see rural producers in stereotypical, black, and white terms: As old white men who collect government subsidies, over-use pesticides and fertilizers, and willy-nilly promote genetically engineered foods. As one activist put it, this seems like a negative world of “guns, trucks, big hair–and even bigger belt buckles.” But after spending two long, hard days participating in a “detassling” corn experiment, another urban food activist reported that he felt inspired to learn more about rural struggles: “It wasn’t very compassionate, what I said when we first talked … had I not detassled [the corn], I might not have gotten there.”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".