PERCEPTUAL BARRIERS PREVENTING SMALL BUSINESS OWNERS FROM USING PUBLIC SUPPORT SERVICES: EVIDENCE FROM CANADA
Bibliographic record
Abstract
Given the economic importance of small and medium enterprises (SMEs), public authorities are seeking ways to improve support for SME owner-managers. Each year, considerable funds are budgeted to help small businesses, and the number of SME support organizations has steadily grown. Despite all these efforts, however, SME survival rates have barely improved, if at all. There may be a good reason for this: entrepreneurs and SME owner-managers do not make maximum use of the services available. Results from a survey of 70 SME owner-managers show that the main barrier is lack of knowledge about the agencies providing the services. On the one hand, many owner-managers do not seem to understand the utility or relevance of the services, while on the other they do not seem to know enough about the agencies working in their region. However, most of the owner-managers who had used the agencies felt the services they had received were appropriate to their needs. At first glance, the problem thus appears to lie more with the perceptions of certain owner-managers than with the nature or quality of the services themselves.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".