Tales of Temptation: Staff and student reflections on AI and dishonesty in the neoliberal academy

The university's experience with AI is a turbulent zone between trust, panic and fascination. Some universities are updating their guidelines to better advise staff and students on ‘acceptable’ ways to collaborate with AI, while others are looking for strategies to restrict AI in their classrooms. At the heart of this unsettling debate are staff and students who are inserted into a growing discourse of ‘cheating culture’. We believe that any discussion in this area would be incomplete without understanding staff’s and students’ lived experiences and reflections on AI. Using social fiction, an arts-based method, this chapter explores how students and staff make sense of and deal with this increasingly complex realm of AI as well as the emotional stress, issues of inequality and ethical concerns they face

Some Hidden Traps of Confidence Intervals in Medical Image Segmentation: Coverage Issues

Medical imaging AI models are usually assessed by reporting an empirical summary statistic of the performance metric, most commonly the mean or median. Recent work has shown that most studies overlook the uncertainty of these estimates, potentially leading to misleading conclusions and hampering clinical translation of medical imaging AI models. To address this issue, systematic reporting of confidence intervals (CIs) has been recommended, but numerous different CI methods exist, and there is very little literature on their behavior in medical imaging. A fundamental property of a CI method is its coverage. This paper contributes towards filling this literature gap in the context of medical image segmentation, studying the coverage of five CI methods for the two arguably most common summary statistics, the mean and the median. To that purpose, we perform a large-scale analysis of CI coverage using non-parametric simulations based on benchmarks instances representing diverse real-world distributions of two common segmentation metrics (Dice similarity coefficient and normalized surface distance). For the mean, all CI methods have decent coverage for most instances when sample sizes exceed 50, even though there are exceptions. For CIs of the median, we unveil major pitfalls: two common bootstrap CI methods have a catastrophic behavior on average whereas another only fails on very degenerate distributions. We believe these pitfalls are important to communicate to the community and that these findings will contribute to future efforts to provide standardized guidelines on confidence interval reporting in medical imaging AI

Using CMAC Staining for Vacuole Characterization in Yeast

CMAC (7-amino-4-chloromethylcoumarin) staining is a valuable tool for visualizing acidic organelles, including vacuoles, in yeast cells. By selectively accumulating in acidic compartments, CMAC allows specific labeling and visualization of vacuoles in living or fixed cells, aiding in the investigation of vacuole dynamics, morphology, and distribution under various conditions. This staining method provides a clear contrast against cellular autofluorescence, enhancing imaging clarity. It can be combined with automated imaging capture for faster throughput and analysis.Characterizing vacuole phenotypes using CMAC staining contributes to our understanding of cellular homeostasis and disease mechanisms in yeast. It has applications in studying fundamental cellular processes, including endocytic trafficking and autophagy, as well as in drug screening assays, and can be used in yeast models of disease to offer insights into potential therapeutic targets for diseases affecting the lysosome, including neurodegenerative diseases

Who is concerned about digitalization? The role of digital literacy and exposure across 30 countries

Rapid digitalization has unleashed widespread digital concerns, namely concerns about the potential harms associated with digital technology use, such as privacy loss, blurred work-family boundaries, and misinformation. Analyzing nationally representative data from the European Social Survey (2020–2022; N = 49,665), we present the first evidence across 30 countries on the prevalence of and sociodemographic variations in digital concerns, as well as how digital literacy and exposure relate to these concerns. Our findings reveal high levels of digital concerns, averaging 0.65 across countries on a 0–1 scale, ranging from 0.47 in Bulgaria to 0.74 in the Netherlands. Following a concave age pattern, adults aged 25–44 years report greater concerns compared to younger people and older adults. More educated individuals report greater digital concerns than those with less education. Digital concerns, however, vary little across the income spectrum or from big cities to remote villages. Exhibiting a positive digital literacy–concern link, those with greater digital literacy are more concerned about digital technologies’ potential harms. This digital literacy–concern link intensifies with digital exposure, which is measured through both individual-level technology use and country-level internet coverage. Our study highlights digital concerns as an understudied yet prominent feature of everyday life in today’s societies. Global agendas for improving digital literacy and engagement should incorporate efforts to address not just digital technologies’ ramifications but also people’s concerns about them

The City as Text

Urban researchers now have access to vast amounts of textual data—from social media and news to planning documents and property listings. These textual data provide important information about the activities of people and organizations in urban environments. Meanwhile, recent advancements in computational tools, including large language models, have expanded our ability to analyze textual data. This article explores how these tools are reshaping the ways we analyse, understand, and theorise the city through text. By outlining key developments, applications, and challenges, it argues that text is no longer a ‘fringe resource’ but a central component in urban analytics with the potential to connect quantitative and qualitative researchers