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Record W1518852504 · doi:10.34989/swp-2008-41

Are There Canada-U.S. Differences in SME Financing?

2021· preprint· en· W1518852504 on OpenAlexaffabout
Danny Leung, Césaire Meh, Yaz Terajima

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsBank of Canada
Fundersnot available
KeywordsFinanceBusinessRisk financingExternal financingRisk managementDebt

Abstract

fetched live from OpenAlex

Previous surveys of Canadian and U.S. business owners suggest that access to financing in Canada may be more problematic than in the United States. Using the 2003 Survey of Small Business Financing in the United States and the 2004 Survey on Financing of Small and Medium Enterprises in Canada, this paper examines whether this perception can be better quantified. Compared to U.S. SMEs, Canadian SMEs are found to have greater reliance on loans from individuals (family, friends and others) and less reliance on loans from financial institutions. This result can be interpreted either as indicative of lower availability of formal credit in Canada, or a lower need for formal credit. Furthermore, while evidence validating the perception that Canadian financial institutions are less likely to approve loan application of risky SMEs cannot be found, there is evidence that supports the notion that Canadian financial institutions are following a more uniform pricing policy than U.S. financial institutions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.020
GPT teacher head0.196
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2021
Admission routes2
Has abstractyes

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