Research on the Effects of Media on Women’s Entrepreneurship
Bibliographic record
Abstract
The media is a powerful tool for shaping public opinion and can significantly influence women's entrepreneurial decisions. By portraying women entrepreneurs in a positive and egalitarian light, the media can help eliminate the negative effects of gender bias, thereby enhancing society's perception and evaluation of women entrepreneurs. Therefore, examining the impact of modern media on women entrepreneurs in contemporary society is a complex and significant issue. Focusing on the growing importance of this topic, the paper aims to explore the impact of media coverage on the image and status of women entrepreneurs. The impact is identified through questionnaires, in-depth interviews, and media content analysis. It conducts an online questionnaire survey and in-depth interviews with 300 women entrepreneurs and 20 women entrepreneurs from different industries. The results showes that over 65% of the respondents believed that media bias had a negative impact on their entrepreneurial spirit, and almost 70% felt that their businesses did not receive fair coverage. The findings confirm that the media’s gendered portrayal of women entrepreneurs affects not only public perceptions of their capabilities and potential but also challenges their confidence and willingness to start their own businesses.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".