Profiling a New Generation of Female Small Business Owners in New Zealand: Networking, Mentoring and Growth
Bibliographic record
Abstract
The contribution of female small business owners to economic development in Western developed countries such as New Zealand, Australia, the United Kingdom, the United States and Canada, is generally under–researched and traditionally grounded in male norms. Increasingly policy–makers acknowledge that in countries like New Zealand where 85% of business employs five or less people, small business offers the greatest employment potential. Not enough is known, though, about the growth orientation and characteristics of female small business owners. This article reports findings from the largest empirical study of small business undertaken in New Zealand and provides inter–gender comparison between male and female small business owners and for intra–gender contrast between networked female small business owners and women who did not belong to a business network. The results showed that the networked women, who were in the main better educated and more affiliative by nature, were more expansionist than both other female small business owners and men. The networked women were also more likely to have a business mentor. The findings confound earlier research suggesting women are less growth–orientated and wish only to satisfy intrinsic needs from their businesses. The article concludes by discussing the need to acknowledge the heterogeneity of female small business and what this means for policy–makers when assessing their socio–economic potential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".