Food and Nutrition as Seen Through the Lenses of the “European Health and Well-being Deal” Discourse
Bibliographic record
Abstract
First proposed in the 1970s, health literacy and well-being have been viewed as “the ability of an individual to obtain and translate knowledge and information in order to maintain and improve health in a way that is appropriate to the individual and system contexts” (Liu et al., 2020, p. 1). Yet, the World Health Organization (WHO) makes clear that health literacy and well-being are more than “just a personal resource”, in that “equal attention [has to] be given to ensure that governments and health systems present clear, accurate, appropriate and accessible information for diverse audiences” (WHO, 2025). This study examines the European health and well-being deal (hereafter EHaW-BD) initiative, which includes a manifesto issued by the coalition All Policies for a Healthy Europe (AP4aHE) (EHMA, 2025) to promote an efficient and trustworthy health system. Through a mixed-method analysis combining genre-based and multimodal tools, as well as insights from discourse analysis (Sarangi & Coulthard, 2000; Bhatia, 2008; Kress, 2009), the manifesto is shown to reveal the EU’s discursive process of conceptualizing food and nutrition as a critical social determinant of health and well-being, while simultaneously distancing this discourse from the EU’s fight against misinformation and disinformation, an effort discursively constructed as part of the Union’s reorientation towards health promotion. Eventually, health literacy and well-being as seen through food and nutrition discourses might be understood in a broader perspective which reflects individual knowledge of healthcare, the health system and policy-making (Okan et al., 2019; Chrysagis & Kompatsiaris, 2023).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 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".