Solutions to the Issues Raised From Korea’s Food With Functional Claims System
Bibliographic record
Abstract
Major countries each have a type of foods with health claims (FHCs).For example, in the United States, there are 'dietary supplements'; in China, 'health foods'; in the EU, 'food supplements'; and in Canada, 'natural health foods.'Each of these countries regulates its respective FHCs under a single law.However, Korea, like Japan, has 2 types of FHCs: 'health functional foods (HFFs)' and 'foods with function claims (FFCs).' HFFs are regulated under the Health Functional Foods Act, 2002, where the Ministry of Food and Drug Safety (MFDS) reviews and approves HFFs based on experimental data submitted by manufacturers regarding the product's functionality and safety.On the other hand, FFCs are governed by the Act on Labeling and Advertising of Foods, 2020.FFCs are the general foods to which more than 30% of the daily recommended intake of HFF components has been added, and the resulting foods are approved by the MFDS without review, simply by reporting the product.However, since the implementation of the FFC system, numerous issues have been raised.This study aims to address these problems.To do so, we analyzed the FHC systems of the major counties.Based on this analysis, we propose 2 possible solutions.Solution A is to abolish the regulation of FFCs.The rationale is that FFCs, due to their insufficient levels of functional ingredients, do not contribute meaningfully to consumer health.Additionally, major countries only maintain one type of FHC.Solution B is to enact a law that integrates the regulation of both HFFs and FFCs.Under the unified regulatory system, these 2 types of products could be managed more efficiently.This law should include the following 3 elements: 1) Change the name from 'foods with function claims (FFC)' to 'foods containing health functional food (FCHFFs)'; because the current FFCs do not guarantee the functionality of HFFs due to their insufficient content but contain it.2) Limit the formula of the FCHFFs to drinks, snacks, or candies, rather than tablets or granules, to distinguish them from HFFs. 3) Include 'fresh foods containing health functional ingredients (FFCHFIs)', i.e. if agriculture or marine foods include health functional ingredients that meet standard amount, they can be approved as FFCHFIs.Solution B is deemed more realistic and, if implemented, is expected to help resolve the current issues with FFCs.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".