USE of Government Assisted Financing by SMES: An Empirical Examination of Canadian SMES using the Financial Growth Cycle
Bibliographic record
Abstract
This study examines Small and Mid-sized Enterprises (SMEs) through the lens of the financial growth cycle, which emphasizes that an entity’s financing options change as they change in size, age, and informational transparency. For this study, we examined SMEs in the city of Corner Brook, the second largest city in the province of Newfoundland & Labrador, analyzing their sources of capital while specifically focusing on two main attributes, size and age. Utilizing the financial growth cycle model, we examined how efficiently SME financing needs are being met through the various options available to them as they grow in size and age. We discuss in particular, findings related to government assisted financing, concluding that as SMEs grows older, they are increasingly likely to opt for federally funded financing as a source of capital. In this study, we sampled SMEs in one city (Corner Brook) to empirically test the financial growth cycle paradigm. The paper investigates the role that government sponsored financing plays in overcoming the information opacity problem of SMEs at different stages of the growth cycle. The study observes that government sponsored financing appears to play an important role in SME financing. The finding suggests that firms should consider accessing sources of government financing even at an early stage in the financial growth cycle.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".