Tasting the alternative: plant-based meat, consumer captivation and the capitalization of plants in food markets
Bibliographic record
Abstract
How do new companies attempt to captivate customers in a food market they are substantially seeking to transform? Plant-based meat has been marketed as a revolutionary food product that will not only transform markets but also provide better health and reduced environmental harms. Building on conceptualizations of captivation, we analyze video ads from leading brands. Our results highlight four modalities of consumer captivation that we refer to as (a) Cali-ology, emphasizing a “West Coast” lifestyle filled by lively gatherings of people enjoying food together; (b) scientism, mobilizing technology and turning chemistry into magic; (c) celebritism, emphasizing health, athletes and a-list celebrities as supporters of plant-based meat; and (d) tasteology, defining sensory knowledge and pleasure as developed by social practices and what is inside foodstuffs. Inferring a transition from matters of concern, such as health and environment, to consumers’ sensory pleasure, these modalities together make up what we term plant capitalization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".