Government Sanctioned Health Claims, Regulations and Economic Policies: Strategies to Increase Consumption of Healthy Functional Foods in Canada
Bibliographic record
Abstract
Non-communicable diseases are chronic diseases that have significant economic and social burdens worldwide. Increased public awareness about the interaction between diet and health, a desire to improve well-being, and government efforts to reduce rising public healthcare costs have been major drivers of substantial growth in the healthy functional foods industry. This paper examines economic policies and regulations that could increase the consumption of healthy functional foods in Canada. It assesses the effectiveness of current public policies and regulations on consumer preferences/demand and considers alternative policies and regulations that could increase the development and growth of the healthy functional food industry, individual wellbeing, reduce health care costs, and stimulate an overall increase in social welfare. It is shown that the use of health claims and educational efforts alone are not enough to increase demand to the socially optimal level. The government needs to use stronger economic and public policies such as tax credits to consumers or subsidies on costs of production that provide consumers with additional incentives to consume healthier foods at the socially optimal level. Keywords: Functional/Health Foods, Market Failures, Health Claims, Economic Policies and Regulations
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".