Bibliographic record
Abstract
Purpose The paper seeks to extend the findings of Okpara and Wynn and Robson and Obeng related to “barriers to small business growth” by using Canadian data. Design/methodology/approach The study utilized survey research (a non‐experimental field study design). Small business owners from Western Canada were surveyed to gather information. Subjects were asked about their beliefs and feelings regarding barriers to growth of their small businesses. To test the hypotheses, p < 0.05 significance level was used to accept or reject a null hypothesis. Findings The findings of this paper indicate that lack of financing, market challenges, and regulatory issues are perceived as barriers to small business growth in Canada. The results also show that sales level of small firms (“past success”) has positive impact on small business growth in Canada. Research limitations/implications This is an exploratory study to determine perceived barriers to small business growth in Canada, so the findings do not necessarily apply to other North American countries. The present study asks for responses from fixed format, set‐questions survey tools, which could exclude additional factors. Originality/value The findings may be useful for the Canadian governments and small business management advisors.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".