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Record W1993765545 · doi:10.1080/00036840801964658

Understanding the financing of innovation and commercialization: the case of the Canadian functional food and nutraceutical sector

2009· article· en· W1993765545 on OpenAlexaffabout
Deepananda Herath, John Cranfield, Spencer Henson

Bibliographic record

VenueApplied Economics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNutraceuticalCommercializationBusinessEntrepreneurshipFunctional foodCapital (architecture)EconomicsMarketingFinance

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.214
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2009
Admission routes2
Has abstractyes

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