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Research on the Effects of Media on Women’s Entrepreneurship

2024· article· en· W4399299444 on OpenAlexaff

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsWomen entrepreneursPerceptionEntrepreneurshipPublic opinionMedia coveragePublic relationsPsychologyPolitical scienceBusinessSocial psychologySociologyMedia studiesPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.348
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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