Bibliographic record
Abstract
Purpose To draw the frontiers of the functional food universe, to identify concepts that should be included in a broadly accepted functional food definition and to propose a definition. Design/methodology/approach Based on a review of the literature and the Delphi technique with a group of North American and European experts. Findings Four concepts were identified: the nature of food, health benefits, functions and regular consumption. Two dimensions, physiological effects and functional intensity, were developed to define the frontiers of the functional food universe and a definition is suggested. Practical implications A large number of definitions as well as great variations within definitions make it difficult to provide industry partners with robust information on market trends and market potential, or to appropriately protect consumers through legislation. This paper should contribute to the debate surrounding the type of food that should be considered a functional food and surrounding the lack of a common definition for functional foods. Originality/value This paper is the first one, to our knowledge, that attempts to conceptually define the frontiers of the functional food universe and to provide a definition of functional food which is not sensitive to cultural differences, can accommodate temporal variations and rely on previous knowledge (definition) as well as experts' opinions.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".