MétaCan
Menu
Back to cohort
Record W2039936647 · doi:10.1080/13691060600748421

Some evidence of the external financing costs of new technology-based firms in Canada

2006· article· en· W2039936647 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueVenture Capital · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversité LavalCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsCommercializationEquity (law)FinanceBusinessInformation asymmetryAgency costSample (material)Actuarial scienceEconomicsMarketingPolitical science

Abstract

fetched live from OpenAlex

Abstract This exploratory study attempts to estimate the external financing costs (EFCs) for a sample of new technology-based firms (NTBFs). A large body of literature describes the constraints these companies face when trying to obtain outside equity from venture capitalists or non-institutional investors. The theory explains some of these difficulties by the prevalence of information asymmetry, agency costs and moral hazard problems. For NTBFs, these phenomena cause the search for outside equity to be a time-consuming, costly process: the EFCs should thus be considerable, but are a largely unexplored aspect of the small business financing problem. We propose an estimation of these EFCs. Some of these costs are not reported in the financial statements and can be determined only through a field survey and case analyses. In this study, we identify the elements that generate the EFCs and estimate the time frames and costs associated with 18 financing rounds undertaken by 12 NTBFs in Canada. We show that these costs are indeed substantial and heavily penalize small companies, especially during the initial financing round and prior to the commercialization phase. Based on our initial propositions and observations, we conclude that the EFCs are higher for the first round of financing, for companies that have not reached the commercialization stage, and are lower as gross proceeds increase.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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