Moral Entrepreneurialism for the Hamburger: Strategies for Marketing a Contested Fast Food
Bibliographic record
Abstract
Recent research has extended the concept of moral entrepreneurialism to corporate actors. We build on this research to investigate how corporations succeed in this effort by uncovering the strategies and tools they employ as moral entrepreneurs. To do so, we examine the corporate discourse of three prominent fast-food firms to identify how they present hamburgers as good food, in a context where beef is increasingly criticized as morally suspect. Based on a discourse analysis of corporate communications and marketing campaigns, we identify three distinct discursive strategies for managing meat criticisms: (1) global managerialism (McDonald's); (2) aestheticized simplicity (A&W); and (3) nostalgic, personalized appeals (Wendy's). These strategies are realized through the use of informational tools to shape what customers think and know about beef, and affective tools to influence how customers feel about beef. Together, these corporate strategies speak to the skilful ability of corporate actors to respond to socio-environmental criticisms. Our case shows how fast-food market actors are able to incorporate critique and offer messages that seek to allow people to feel good about eating beef. This case is relevant to understanding the tools that corporations use to be effective moral entrepreneurs. It also provides a deeper understanding of marketing discourse at the nexus of social problems and consumption choices.
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 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".