The Meaning of 'Meat': Boundary Objects in the Promotional Cultures of Plant-Based Meat
Bibliographic record
Abstract
This dissertation interrogates the use of boundary objects in the rhetorical framing strategies of plant-based meat companies. I address the framing, categorization, and boundary work of foods such as "vegan", "meat", and "burger" from a Bourdieuian, class-focused perspective. I use rhetorical framing analysis to critically engage with the Beyond Meat and Impossible Foods burger campaigns--including CEO interviews (with Patrick Brown and Ethan Brown), trade journal articles (Fortune and Business Insider), company websites (Beyond Meat, Impossible Foods, A&W, and Burger King), Twitter posts (@AWCanada and @BurgerKing), and consumer engagements on Twitter--between 2014-2018. This project contributes to the theoretical refinement of boundary objects by demonstrating how they can be used to rhetorically situate non-dominant social actors within the categorical boundaries of dominant groups. In this case, plant-based meat companies redefine "meat" along chemical and nutritional lines in order to situate themselves and their products within the privileged socio-cultural category of meat. I also enhance the usefulness of rhetorical framing analysis as a method of studying communication by adding agenda-dismissal to the methodological repertoire of agenda-setting theory. I find that, while vegetarianism and veganism have historically constituted anti-consumerist subjectivities, Beyond Meat, Impossible Foods, and the rhetoric of plant-based meat serve to reinforce a dominant ideological frame of individuals-as-consumers by encouraging people to consume more "good" food. From a class-based perspective, this rhetorical strategy places the consumerist logic of plant-based meats at odds with the conspicuous and distinguished consumption ideals of bourgeois veganism. Beyond Meat, Impossible Foods, and plant-based meat rhetorics thus perpetuate neoliberal hegemony by emphasizing a nutricentric framing of food products, which dismisses other relevant social, cultural, and economic elements of food. Finally, I use this project as an interjection into the larger field of cultural studies in order to identify and name an emerging sub-discipline of critical analysis: "meat studies". Ultimately, I argue that the rhetoric of plant-based meat companies reinforce rather than challenge both meat's privileged cultural status and the foundations upon which consumer capitalism exists.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".