Obfuscating Ill-being and Creating Well-being: The Communication Strategies of Five Top Food and Beverage Companies
Bibliographic record
Abstract
While research in the field of nutrition and health has been showing for decades that what we consume is fundamental for our health (see World Health Organization, 2025), the largest food and beverage companies in the world are still not relenting in their preparation of products that are harmful for our well-being. Clearly helped along by the fact that the term “well-being” is no longer just limited to physical health but can include “happiness, life satisfaction, personal strengths, and flourishing” (Holder, 2019, p. 1), these companies adopt communication strategies that arouse a positive psychological sentiment, obfuscating the health-related issues. It is the aim of this paper to analyse some of the linguistic strategies that five of the largest food and beverage companies in the world use to create psychological well-being regarding food and drink intake. More precisely, by applying the methodology of Critical Discourse Analysis and by focussing in particular on word connotations, the choice of adjectives, and style of communication, the websites of JBS Foods, Nestlé, PepsiCo, The Coca-Cola Company, and Tyson Foods will be examined in order to unveil the linguistic and stylistic devices that foster a positive sentiment towards some food and drinks that scientific research has shown to be harmful.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".