Inaugurele rede voor de Universiteit van Maastricht met als onderwerp publieke gesprekken over gender en biotechnologie.
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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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The aim of the study is to investigate the influence of gender and gender pairing on students’ learning performances and knowledge elaboration processes in Computer-Supported Collaborative Learning (CSCL). A sample of ninety-six secondary school students, participated in a two-week experiment.Students were randomly paired and asked to solve several moderately structured problems concerning Newtonian mechanics. Students’ pretest and posttest performances were analyzed to see whether students’ gender and the gender pairing (mixed or single-gender) were significant factors in their problem solving learning in CSCL. Students’ online interactions were also analyzed to unravel the dynamic process of individual knowledge elaboration. The multilevel analyses revealed that a divergent pattern of knowledge elaboration was a significant predictor for students’ learning achievement, and in mixed-gender dyads students’ knowledge elaboration processes were more inclined to diverge from each other. Moreover, females in single-gender dyads significantly outperformed females in mixed-gender dyads. But this was not the case for male students.
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Affective teacher–child relationships have frequently been investigated in school settings, but less attention has been devoted to these relationships in after-school care. This study explored caregiver- (N = 90) and child-informed reports (N = 90) of the affective caregiver–child relationship (N = 180 dyads) in Dutch after-school care, exploring gender differences at caregiver and child level and the relationship with a gender match between children and caregivers. The caregivers and children reported relatively high levels of closeness and relatively low level of conflict and dependency/autonomy support, irrespective of gender. Multilevel regression analyses revealed that a gender match between child and caregiver was associated with teacher-reported closeness: levels were highest in female-girl dyads and lowest in male-boy dyads. Further, boys indicated the highest levels of autonomy in male-boy dyads, whereas girls indicated the lowest levels in female-girl dyads. Masculinity of staff was associated with more child-reported autonomy support, whereas femininity predicted caregiver-reported closeness in the relationship.
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This chapter applies the theory of symbolic interactionism to present the qualitative analysis of 51 sentencing decisions in domestic violence homicide cases processed in Polish courts. It is argued in this chapter that sentencing, like any other human action, is subject to interpretations at the hands of judges, who engage in the construction and meaning-making process of gender at the sentencing stage. The findings demonstrate the diversely-constructed presence of gendered narratives, which vary in terms of their inclusion of the domestic violence terminology and/or the discussion on the fulfilment of socially-prescribed gender roles. The analysis has exposed a powerful interplay between the judges’ perception of the (abusive) relationship, gender roles, and the defendant’s/victim’s acquiescence to them, which in consequence makes women more likely the subject of double standards of conformity. The chapter offers a qualitative outlook on the topic and invites a new theoretical perspective to shift the attention from the impact of gender – to the meaning of gender – in sentencing decisions.
MULTIFILE
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.
MULTIFILE
Research has shown that female students cannot profit as much as male students can from cooperative learning in physics, especially in mixed-gender dyads. This study has explored the influence of partner gender on female students’ learning achievement, interaction and the problem-solving process during cooperative learning. In Shanghai, a total of 50 students (26 females and 24 males), drawn from two classes of a high school, took part in the study. Students were randomly paired, and there were three research groups: mixed-gender dyads (MG), female–female dyads (FF) and male–male dyads (MM). Analysis of students’ pre- and post-test performances revealed that female students in the single-gender condition solved physics problems more effectively than did those in the mixed-gender condition, while the same was not the case for male students. We further explored the differences between female and male communication styles, and content among the three research groups. It showed that the females’ interaction content and problem-solving processes were more sensitive to partner gender than were those for males. This might explain why mixed-gender cooperation in physics disadvantages females in high schools.
MULTIFILE
Important gender differences, relating to trauma history, offending and mental health needs are not sufficiently considered in most (risk) assessment and treatment procedures in forensic practice. We developed guidelines for gender-responsive work in Dutch forensic mental health care. The experiences of practitioners and forensic psychiatric patients were collected and analyzed by means of an online survey (n = 295), interviews with professionals (n = 22), female (n = 8) and male (n = 3) patients. Guidelines regarding gender-sensitive (risk) assessment and trauma-informed care were rated as most relevant in the survey. In the interviews we focused on experiences and wishes for trauma treatment and gender-mixed treatment. Practical guidelines were written based on the results of the survey, interviews and literature, and presented in expert meetings with patients and practitioners, and further refined based on their comments. Applying these guidelines may contribute to improved treatment for female patients thereby preventing relapse.
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Narrative structures such as the Hero’s Journey and Heroine’s Journey have long influenced how characters, themes, and roles are portrayed in storytelling. When used to guide narrative generation in systems powered by Large Language Models (LLMs), these structures may interact with model-internal biases, reinforcing traditional gender norms. This workshop examines how protagonist gender and narrative structure shape storytelling outcomes in LLM-based storytelling systems. Through hands-on experiments and guided analysis, participants will explore gender representation in LLM-generated stories, perform counterfactual modifications, and evaluate how narrative interpretations shift when character gender is altered. The workshop aims to foster interdisciplinary collaborations, inspire novel methodologies, and advance research on fair and inclusive AI-driven storytelling in games and interactive media.
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Substance abuse is an important risk factor for offending, but is mostly studied in males. The aim of this multicentre study is to gain insight into possible gender differences in substance abuse history and offending behavior in forensic psychiatric patients. Files were analyzed of 275 women and 275 matched men who have been admitted between 1984 and 2014 to one of four Dutch gender-mixed forensic hospitals. Risk assessment ratings were related to registered violent incidents during treatment for both women and men and to official recidivism data after discharge for a subgroup of 78 women. Substance abuse history was coded as a risk factor for more than half of the women (56.7%), but significantly more often for men (68.4%). Men were more often diagnosed with substance dependence and more frequently committed the index-offense whilst intoxicated. Prediction of violent incidents during treatment was more accurate for men. A history of substance abuse was not a significant predictor for recidivism after discharge in the subgroup women. It is concluded that there are gender differences in substance abuse history and that the relationship with offending behavior seems stronger for men. These differences have implications for substance use treatment in forensic mental health services.
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