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Record W3200825557 · doi:10.1177/17499755211039932

Moral Entrepreneurialism for the Hamburger: Strategies for Marketing a Contested Fast Food

2021· article· en· W3200825557 on OpenAlex
Natália Otto, Josée Johnston, Shyon Baumann

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCultural Sociology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFood marketingMarketingSociologyAdvertisingBusinessEnvironmental ethics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.245
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it