Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, women ages 50+ pursued their dream of starting a business. This study contacted 278 women ages 50-plus by phone and online, examining the drivers and barriers of women entrepreneurs. Over a quarter (27%) of women said they always wanted to start a business, and 19% said they did it to follow their passion; another 17% were pursuing additional income, and 11% wanted flexible work options. Age, and perhaps the value of experience, has been an advantage in business ownership. Women entrepreneurs age 50-plus were less likely to have faced financial challenges since starting their business, with over two in five (45%) avoiding such challenges, compared to 29% of women entrepreneurs in their 40s. Nearly seven in 10 women (69%) surveyed poured their personal savings into their start-up. In addition, two in three agree that they face unique challenges in trying to access capital for their business that are different from men. Despite these challenges, most women were optimistic about their entrepreneurial path. The majority of women (97%) agreed that they made the right decision in starting their business – with about two in five (39%) saying their business is doing better than expected compared to when they first started. Respondents say they need resources on marketing, recruiting and hiring staff, and financing. And over two in five say they have not taken any type of training. Increasing awareness of business supports, funding sources, and training opportunities will help women as they grow their business.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".