WOMEN ENTREPRENEURS: CHALLENGES AND OPPORTUNITIES
Bibliographic record
Abstract
Women entrepreneurs are considered as main players in any developing country, predominantly in contribution to economic development. In recent years, even with the developed countries namely USA and Canada, Women entrepreneur’s roles in terms of their share in small business has been increased. Women entrepreneur faces a various problems at different stages starting from their initial inauguration of enterprise, in running their enterprise. In sort situation, women might feel as though task which they needed to adopt a stereotypically male attitude toward business like competitive, forceful and sometimes very cruel. In this context the successful female CEOs judge that remaining true to yourself and result your own voice are the keys to growing above preconceived expectations. Be yourself, and have confidence in who you are, said Hilary Genga, originator and CEO of women's swimwear company Trunkettes. You made it to where you are through hard work and perseverance, but most importantly, you're there. Don't conform yourself to a man's idea of what a leader should look like. This paper analyzed the bitter situations faced by the Women entrepreneurs and the suggestion to overcome from such situation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".