PurposeAll entrepreneurs interact simultaneously with multiple entrepreneurial contexts throughout their entrepreneurial journey. This conceptual paper has two central aims: (1) it synthesises the current literature on gender and entrepreneurship, and (2) it increases our understanding of how gender norms, contextual embeddedness and (in)equality mechanisms interact within contexts. Illustrative contexts that are discussed include entrepreneurship education, business networks and finance.Design/methodology/approachThis conceptual paper draws upon extant literature to develop its proposed conceptual framework. It provides suggestions for systemic policy interventions as well as pointing to promising paths for future research.FindingsA literature-generated conceptual framework is developed to explain and address the systemic barriers faced by opportunity-driven women as they engage in entrepreneurial contexts. This conceptual framework visualises the interplay between gender norms, contextual embeddedness and inequality mechanisms to explain systemic disparities. An extra dimension is integrated in the framework to account for the power of agency within women and with others, whereby agency, either individually or collectively, may disrupt and subvert the current interplay with inequality mechanisms.Originality/valueThis work advances understanding of the underrepresentation of women entrepreneurs. The paper offers a conceptual framework that provides policymakers with a useful tool to understand how to intervene and increase contextual embeddedness for all entrepreneurs. Additionally, this paper suggests moving beyond “fixing” women entrepreneurs and points towards disrupting systemic disparities to accomplish this contextual embeddedness for all entrepreneurs. By doing so, this research adds to academic knowledge on the construction and reconstruction of gender in the field of entrepreneurship.
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The moment of casting is a crucial one in any media production. Casting the ‘right’ person shapes the narrative as much as the way in which the final product might be received by critics and audiences. For this article, casting—as the moment in which gender is hypervisible in its complex intersectional entanglement with class, race and sexuality—will be our gateway to exploring the dynamics of discussion of gender conventions and how we, as feminist scholars, might manoeuvre. To do so, we will test and triangulate three different forms of ethnographically inspired inquiry: 1) ‘collaborative autoethnography,’ to discuss male-to-female gender-bending comedies from the 1980s and 1990s, 2) ‘netnography’ of online discussions about the (potential) recasting of gendered legacy roles from Doctor Who to Mary Poppins, and 3) textual media analysis of content focusing on the casting of cisgender actors for transgender roles. Exploring the affordances and challenges of these three methods underlines the duty of care that is essential to feminist audience research. Moving across personal and anonymous, ‘real’ and ‘virtual,’ popular and professional discussion highlights how gender has been used and continues to be instrumentalised in lived audience experience and in audience research.
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This article investigates gender bias in narratives generated by Large Language Models (LLMs) through a two-phase study. Building on our existing work in narrative generation, we employ a structured methodology to analyze the influence of protagonist gender on both the generation and classification of fictional stories. In Phase 1, factual narratives were generated using six LLMs, guided by predefined narrative structures (Hero's Journey and Heroine's Journey). Gender bias was quantified through specialized metrics and statistical analyses, revealing significant disparities in protagonist gender distribution and associations with narrative archetypes. In Phase 2, counterfactual narratives were constructed by altering the protagonists’ genders while preserving all other narrative elements. These narratives were then classified by the same LLMs to assess how gender influences their interpretation of narrative structures. Results indicate that LLMs exhibit difficulty in disentangling the protagonist's gender from the narrative structure, often using gender as a heuristic to classify stories. Male protagonists in emotionally driven narratives were frequently misclassified as following the Heroine's Journey, while female protagonists in logic-driven conflicts were misclassified as adhering to the Hero's Journey. These findings provide empirical evidence of embedded gender biases in LLM-generated narratives, highlighting the need for bias mitigation strategies in AI-driven storytelling to promote diversity and inclusivity in computational narrative generation.
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