Notice bibliographique
Résumé
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.”
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».