Opting for Growth: Gender Dimensions of Choosing Enterprise Development
Bibliographic record
Abstract
Abstract This study documents how owners of small businesses arrive at growth objectives for their firms. The decision to pursue (or not) a growth objective involves trade‐offs among both financial and non‐financial factors. Owners' growth decisions appear to be shaped by attitudes towards owners' perceived outcomes of growth and the opinions (about growth) of important others in the owners' lives. Male and female owners exhibit strong similarities in how they arrive at growth decisions. However, female business owners appear to accord relatively more weight to their needs for a supportive managerial and spousal setting and to be discouraged to a relatively greater degree by the growth‐related stress associated with personal demands made on their time and family. Résumé Cette étude documente la façon dont les propriétaires de petites entreprises formulent des objectifs d'expansion pour leur firme. La décision de suivre (ou de ne pas suivre) un plan donné comprend des facteurs financiers et non‐financiers. Les décisions d'expansion que prennent les propriétaires semblent dictées par les attitudes et la perception qu'ils ont au sujet de la croissance de leur propre entreprise. Elles sont aussi dictées par les opinions des personnes que les propriétaires ont en estime. Il existe de grandes similarités entre la façon dont les hommes propriétaires d'entreprise et les femmes propriétaires d'entreprise arrivent aux décisions concernant la croissance de leur entreprise. Mais, les propriétaires femmes semblent avoir relativement plus besoin d'une situation directoriale et familiale stimulante et sont découragées à un niveau relativement plus élevé par le stress lié à la croissance et les exigences faites sur leur temps et leur famille.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".