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Record W4409350683 · doi:10.1002/agr.22049

Access to Finance and Innovation in the Canadian Food Processing

2025· article· en· W4409350683 on OpenAlex
Getu Hailu, Deepananda Herath

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAgribusiness · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersOntario Agri-Food Innovation AllianceAgriculture and Agri-Food CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsBusinessFood processingEconomicsIndustrial organizationFinancePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT Innovation is a presumed channel through which finance affects productivity, yet there is limited research testing the relationship between finance and innovation in the food manufacturing sector. The purpose of the paper is to explore the determinants (e.g., financing, R&D, firm size, expenditure on innovation) of the adoption of innovation. We use data from the 2018 Innovation in the Food Processing Industry Survey in Canada to examine the relationships between innovation activities and access to finance for these activities. The results show that access to financing is one of the key drivers of innovation activities of Canadian food processors. Access to finance is also correlated with innovation performance across firms in each type of innovation, which is consistent with the findings of previous studies. The results have implications for policies targeted at enhancing R&D and innovation in the food processing sector.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
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 head0.048
GPT teacher head0.258
Teacher spread0.210 · 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