Firm, market, and regulatory factors influencing innovation and commercialization in Canada's functional food and nutraceutical sector
Bibliographic record
Abstract
Abstract Factors influencing the development and commercialization of functional food and nutraceutical (FFN) products are explored. Count data models are developed to relate firm, market, and regulatory covariates to the number of FFN product lines firms have under development, on the market, and in total. Canadian firm‐level innovation data were taken from Statistics Canada (2003) Functional Food and Nutraceutical Survey. Firms involved in product development/scale‐up had more product lines in total and on the market. Firms with a strong and positive perception of the impact of regulatory reform related to generic health claims and harmonization of Canadian regulations with U.S. regulations had fewer product lines in total and on the market. Firms with more positive perceptions of the business impact of structure and function health claims had more product lines on the market. One implication of the study is the importance of developing policies and reforming regulations which better enable use of generic health claims on FFN products. Further, policies which better enable or foster development/scale‐up of product lines would increase the Canadian FFN sector's ability to develop new products. [EconLit: O130, L500, Q180]. © 2008 Wiley Periodicals, Inc.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".