Understanding the financing of innovation and commercialization: the case of the Canadian functional food and nutraceutical sector
Bibliographic record
Abstract
We develop and implement two models to show what factors affect a firm's decision to seek external financing and the level of financing obtained in the Canadian functional food and nutraceutical sector. Data from a national survey of functional foods and nutraceutical firms in Canada, conducted by Statistics Canada in 2003, is used for this analysis. Firm size, being privately held and engaging in contractual arrangements have negative impacts on the likelihood of a firm seeking external funding, while firms which are intensively involved in the functional food and nutraceutical sector, with greater prospects for business expansion and/or involved in partnerships are more likely to seek external financing. Larger firms and those involved in functional food and nutraceutical research and development receive a greater amount of capital when they decide to raise capital. However, firms focused on functional foods and nutraceuticals, as opposed to more diversified firms, and those involved in product development and concept scale-up, receive less capital. Our findings highlight the importance of public support in addressing the capital requirements of functional food and nutraceutical firms and underscore the considerable burden borne by smaller firms in this respect.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".