From Hungry to Healthy

Abstract

We examine American Cosmopolitan in order to understand how specific foods have been linked to dominant forms of beauty. Three food-beauty nexuses emerge, namely moralism, strategy and holism. To understand how women engaged with these nexuses, we draw on Simmel’s “religiosity.” Simmel traced deeply-felt experiences like self-cultivation (beauty) through cultural objects (food) using religious imagery. In this respect, changing messages about diets suggest profound encounters with the limits of forms of beauty. But the conflict of culture is also apparent: it is difficult to create new forms of beauty or do away with gendered beauty standards altogether.

Keywords:

• Simmel
• food
• beauty
• diet
• culture
• gender
• religion
• conflict in culture

Acknowledgements

We would like to thank David Panunto for transcribing many of the “Dieter’s Notebooks” columns. We thank the staff of the Toronto Public Library for their assistance with microfiche. We are grateful to Dr. Michelle Szabo and Salina Abji for their comments on an earlier draft, and two anonymous reviewers for their constructive feedback. We thank CAFS’s 2013 attendees for their feedback and Michale Haedicke for an informative 2014 ASA session.

Notes

1. The word “people” is peppered throughout our paper. There are more categories than male and female, and “women” are not the only people to work with messages about feminine beauty. Despite this acknowledgement, we do not critically engage with alternative categories.

2. See Symons 1994 for a rich account of Simmel’s essay.

3. Since transcribing magazines in their entirety was practically infeasible, we decided to transcribe “Dieter’s Notebook” columns for the odd years from 1968–1994, as well as 1968 and 1994 (n = 180) in order to identify any messages about how food has been pitched to beauty.

4. Words may differ in the number of times they are featured, or their weighted percentage, which refers to the prevalence of words with respect to the total word tally. Results are based on NVivo 10’s word frequency application. The statistical examinations are based on R’s text-mining package “tm.”.