Bibliographic record
Abstract
A recent World Bank report suggests that India has the potential for double-digit economic growth if more women actively participate in the country's economy. In today's modern era, women are taking on various roles alongside their male counterparts in both the private and public sectors. The increasing presence of women in organizations signifies the importance of recognizing their contribution to the nation's development. Over time, women's roles have evolved significantly, from traditional societies to the contemporary global landscape. However, there is still much work to be done to encourage women to pursue entrepreneurship. Empowerment, in essence, means ensuring that women have equal status, opportunities, and independence. Women's empowerment, in simple terms, entails granting them the freedom to make decisions for themselves and create a more equitable place for them in society. Two crucial factors necessary for the empowerment of Indian women are education and entrepreneurship. In our country, parents often focus on preparing their daughters for household responsibilities as their primary goal. However, it is equally important to equip them with the skills to earn a living, promoting their financial independence. Encouraging girls to consider entrepreneurship as a viable career option is also essential if they wish to turn their talents into a profession. This study aims to shed light on the challenges and obstacles that women entrepreneurs face in India. Additionally, it seeks to analyze the existing financial support mechanisms and government funding schemes designed to promote women's entrepreneurship. It's worth noting that this study relies on secondary sources. The primary objective is to identify the key factors that can boost the number of women entrepreneurs while providing relevant suggestions for achieving this goal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".