Exploring Small Business Strategies in Halifax, Nova Scotia
Bibliographic record
Abstract
Small business owners contribute 39% of Canada's gross domestic product and account for 67% of new jobs created, but only 50% survive beyond the first 5 years of existence. The purpose of this multiple case study was to explore what strategies some small business owners in Halifax, Nova Scotia used to sustain their business operations beyond the first 5 years. The study population consisted of 6 small business owners of professional firms located in Halifax, Nova Scotia who had succeeded in business beyond the first 5 years. The conceptual framework that grounded this study was the systems theory. Data were collected through semistructured interviews, a review of company documents, and archival records. Member checking of interview response data was used to strengthen the credibility of the findings. Based on the methodological triangulation of the data collected and the van Kaam process, themes that emerged after the data analysis were networking, product-advantage, business-centric approach, and human capital. The data and application of the findings from this study may contribute to social change by providing essential strategies for small business owners to ensure business success that could potentially lead to the prosperity of the community and Halifax economy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".