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The Silent Standpoint: How Professors Explain Gender Disparities in Academia
Margaretha Järvinen, Nanna Mik‐Meyer
The British Journal of Sociology·2026
ABSTRACT
Based on 77 qualitative interviews with professors in higher education, this article explores the interviewees' opinions on how gender disparities in academia should be explained. We show that male professors relate women's career barriers to family factors and women's own interests and preferences. In contrast, female professors favour explanations at the university level, for example lack of recognition of women, implicit bias in evaluations, male networks and an unwelcoming academic culture. Furthermore, we identify a ‘silent standpoint’ among the participating male professors: the idea that women are generally less qualified than men as candidates for full professorships. The article draws on sociological accounting theories, focussing on the ‘excuses’ and ‘justifications’ used by professors when discussing gender issues. Male professors ‘excuse’ gender disparities in academia by referring to women's preferences or ‘justify’ them by appealing to meritocratic standards. Entangled in these ‘neutralising’ accounts is the silent standpoint regarding women's low qualifications, a standpoint, however, that is difficult for male professors to articulate in an interview with a female colleague.
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View paper →Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation
Adriana Alvarado Garcia, Ruyuan Wan, Ozioma C. Oguine, Karla Badillo-Urquiola
arXiv·2026
Recently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial Intelligence. However, most existing work emphasizes technical benchmarks and attack success rates, leaving the socio-technical practices of how red teaming datasets are defined, created, and evaluated under-examined. Drawing on 22 interviews with practitioners who design and evaluate red teaming datasets, we examine the data practices and standards that underpin this work. Because adversarial datasets determine the scope and accuracy of model evaluations, they are critical artifacts for assessing potential harms from large language models. Our contributions are first, empirical evidence of practitioners conceptualizing red teaming and developing and evaluating red teaming datasets. Second, we reflect on how practitioners' conceptualization of risk leads to overlooking the context, interaction type, and user specificity. We conclude with three opportunities for HCI researchers to expand the conceptualization and data practices for red-teaming.
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View paper →The Human Mecha: Titan, Technology, and Self in Attack on Titan
He, Max
escholarship.org·2023
This paper focuses on the figure of the Titans in Hajime Isayama’s best selling manga series Attack on Titan. Grotesque, horrifying, but most certainly entrancing in their violence, I argue that the Titan is a representation of the mecha (humanoid robot) trope in manga and anime, at once human and monster, in form, and technology, in allegory. The connection between human and technology, though known, is often taken for granted. The body of the Titan is where the human and machine meet, the site where their interdependent dynamic is explored. Just as the pilot is necessarily connected to the mecha, humanity is necessarily connected to technology, being its creator, user, and thus the autonomous agent behind its operation. In the doomsday setting of Attack on Titan, the vehicle of the apocalypse is neither explicitly technology nor humanoid machine, but rather the Titan: giant, macrocosmic manifestations of the human. On a larger scale, the Titans become a site of self-reflection, magnifying some of the more neglected aspects and tendencies of human nature.
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