APPROACHES TO THE DEVELOPMENT OF WOMEN'S ENTREPRENEURSHIP: FOREIGN EXPERIENCE
Bibliographic record
Abstract
The article discusses the problems of development of women's entrepreneurship in the world, in Europe and in Canada. It is found that the entrepreneurial activity of women, although increasing in recent years, is still lower than that of men due to the existence of a number of problems, such as: gender stereotypes, unequal access to financial resources, legislative and regulatory obstacles, and others. Governments recognize that the involvement of women in entrepreneurship can bring significant benefits to the economy, and this prompts the development of appropriate public policies and support programs. The European Union has formed a strong regulatory framework in the field of women's economic independence and the development of women's entrepreneurship, introduced a number of tools and initiatives aimed at supporting women entrepreneurs. A number of initiatives are underway in the UK to promote greater transparency in funding distribution, launch new investment vehicles, encourage institutional and private investors, create new banking products, improve access to professional expertise, and expand opportunities for mentoring, entrepreneurial education and awareness. In particular, the Investing in Women Code has been developed, which is a commitment to support the development of women's entrepreneurship in the country by improving the access of women entrepreneurs to the financial services market; The Invest in Women Hub was created for the purpose of information support. Canada has developed and implemented the Women Entrepreneurship Strategy, which aims to develop such areas as helping businesses grow through mentoring and skills development; expanding access to capital; improving access to federal business innovation programs; expanding access to data and knowledge. It is found that the implementation of state policies in the field of women's entrepreneurship leads to positive results and reduction of gender inequality in the economic, social and educational spheres.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".