Women Entrepreneurs in India: Some Observations on their Problems and Prospects
Bibliographic record
Abstract
Economies all over the globe are developing very significantly. One amongst the developing economies is India. The significant growth of the developed economies has been due to the increased entrepreneurship and innovation. A balance can never work without tow hands. In the same way development is brought by both male and female. One point must be noted that the developed nations have never done any bifurcation in promoting male and female entrepreneurs. Women entrepreneurs have always performed well in economies like India, Canada, Germany, America, Europe, and England. Women workforce is as much performing and deserving as the men. In India, majority of the workforce belong to the female gender but very few women are self-employed. Government programmes are promoting the women entrepreneurs but they still undergo various psycho-social factors in the way towards entrepreneurship. Women also lack proper access to financial support by the government and the information back lag problem also resists them in performing well. It must not be ignored that women, nowadays, are performing significantly well in all sectors whether it be games, engineering, electrical, politics or it be the business. Women have always proven to be superior to men; the only thing they require is an opportunity. There is a need for women empowerment in this perspective. Women have always been a mother and will always be not only of the mankind but also the mother of the success and development of an economy. Her role must not be ignored and it must always be elicited that “No success in the World is without Women”.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".