Valuing the Health Benefits of a Novel Functional Food
Bibliographic record
Abstract
Awareness of the link between diet and health has led to an interest from consumers, the food industry, and policymakers in the health properties of foods. Food industry innovations are emerging that enhance the health attributes of foods. Recent scientific evidence has linked the consumption of foods high in trans fatty acids with elevated cholesterol levels and a higher incidence of coronary heart disease. Foods that reduce the risk of chronic diseases have potential social welfare benefits. This article values the potential health benefits of a healthy food: a trans fat‐free canola oil. Using a range of assumptions about the extent to which a trans fat‐free canola oil will substitute for existing oils, the paper shows how two alternative methods of computing reductions in the cost of illness reveal nontrivial benefits to society. Policy implications and suggestions for further research are discussed. La sensibilisation au lien qui existe entre l'alimentation et la santé a suscité chez les consommateurs, l'industrie alimentaire et les décideurs un intérêt pour les propriétés des aliments pour la santé. L'industrie alimentaire ne cesse d'innover afin d'accroître les attributs des aliments pour la santé. Des preuves scientifiques récentes ont établi un lien entre la consommation d'aliments riches en gras trans, des taux de cholestérol élevés et une incidence accrue de coronaropathie. Les aliments qui diminuent les risques de maladies chroniques ont des bienfaits potentiels sur le bien‐être social. Le présent article porte sur l'évaluation des bienfaits potentiels d'un aliment sain, soit une huile de canola sans gras trans. À l'aide de diverses hypothèses quant au degré de remplacement des huiles existantes par l'huile de canola, le présent article montre de quelle fa¸on deux méthodes de calcul des diminutions du coût de la maladie révèlent des avantages non négligeables pour la société. Nous discutons des incidences politiques et présentons des suggestions de recherche ultérieure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".