Berkeley College

This concept paper advances stigma power as a central analytical mechanism for understanding how patriarchy, capitalism, white supremacy, and cis-heteronormativity operate with particular intensity against sex workers. Integrating Link and Phelan’s stigma power with Bourdieu’s symbolic violence and Foucauldian productive power, the framework theorises stigma as a mechanism institutionalised through law and enforced by institutions, which produces measurable consequences that include violence, exclusion, and health harms. Analysing the intersecting axes of gender, sexuality, race, migration, and class across three qualitative studies (SWMH, SEXHUM, VICSW), the article demonstrates why labour-rights reforms, including decriminalisation, are necessary but insufficient. Dismantling stigma requires not only removing sanctions but actively contesting the actors exercising stigma power and interrupting the stabilising mechanisms that reproduce it. This requires policy that acknowledges stigma’s existence whilst working to dismantle it, rather than eliding its reality through liberal mainstreaming or strengthening it through criminalisation or rescue frameworks. The framework explains why decriminalisation is associated with better access to rights and health; why all criminalisation including the so-called Swedish model correlates with increased violence; why stigma persists under optimal legal conditions; and how intersecting marginalisations produce differential vulnerability. Policy implications emphasise pairing decriminalisation with peer-led anti-stigma work, institutional reform, migrant rights, and funded support for sex worker self-organisation.

We prove that the tree-like Deligne-Mumford operad is a homotopical model for the trivialization of the circle in the higher-genus framed little discs operad. Our proof is based on a geometric argument involving nodal annuli. We use as a model for the higher-genus framed little discs an operad of Riemann surfaces with analytically parametrized boundary. We develop the formalism of topological moduli problems as a framework to accommodate the orbifold nature of the Deligne-Mumford operad.

Success! Charlotte and Karthik watched the number of Corg.ly API users increase. Mei had emailed earlier in the week with congratulations and some initial feedback on the documentation and code. The celebrations were over, and, more than anything, Charlotte felt an immense sense of relief and accomplishment.

The sudden increase in multimodal content on social media has greatly changed the spread of rumors, which brings about considerable difficulties for the current detection and management strategies. Although most previous research mainly concentrates on unimodal or static detection techniques, they usually ignore the cross-modal semantic discrepancies and the changing characteristics of post-detection intervention. To solve these problems, this research puts forward a theory-based and computation-oriented approach for managing multimodal rumors. Firstly, we establish a three-level dissemination model which combines user's perception, network organization and algorithmic amplification to describe the process of multimodal rumors changing during their life cycle. On the basis of this theoretical basis, we propose the EFF-RD, a multimodal deep learning system that includes external information and improvement of fake features. This model uses dual contrastive learning to identify semantic incongruities between different modes and applies gated adaptive fusion to flexibly combine various types of features. Moreover, we treat rumor detection as a dynamic management issue by defining the optimization of intervention strategies as a Markov Decision Process and solving it using reinforcement learning. It allows for the adjustment of the timing, strength and kind of intervention. The experimental results on standard data sets show that our method, EFF-RD, performs better than the latest models in terms of detection ability. Furthermore, the proposed intervention strategy is 22.3% more efficient than the static methods. This research enriches the literature by establishing a closed-loop model which links rumor detection and countermeasure, offering both methodical advancements and practical benefits to platform control and anti-false information policies.

Extended Reality (XR) systems have become affordable and easier to use. The availability of easy-to-use consumer devices has led to explorations into the feasibility of integrating XR into higher education teaching and learning. However, adoption of XR in higher education has been limited so far, especially due to the complexities involved in creating XR experiences. This paper outlines emerging ways in which new Generative Artificial Intelligence (GenAI) technologies can overcome some of those barriers by facilitating the XR development processes. The topics of this paper include the role of GenAI in supporting: 1) the coding side of XR development; 2) the generation of 3D content such as objects, scenes, and worlds; 3) the incorporation of intelligent XR agents as interactive characters; and 4) the enhancement of accessibility features. These topics are considered, in turn, from the perspective of how academic technology service models can evolve to support these new GenAI-based approaches to academic XR development.