A mixed-methods approach to understand victimization discourses by opposing feminist sub-groups on social media.

Opposing social movements are groups that have conflicting objectives on a shared social justice issue. To maximize the probability of their movement's success, groups can strategically portray their group in a favourable manner while discrediting their opposition. One such approach involves the construction of victimization discourses. In this research, we combined topic modelling and critical discursive psychology to explore how opposing groups within the feminist movement used victimization as a lens to understand their movements in relation to transgender women. We compiled a dataset of over 40,000 tweets from 14 UK-based feminist accounts that included transgender women as women (the pro-inclusion group) and 13 accounts, that excluded transgender women (the anti-inclusion group). Our results revealed differences in how victimization was employed by the opposing movements: pro-inclusion groups drew on repertoires that created a sense of shared victimhood between cisgender women and transgender women, while anti-inclusion groups invoked a competitive victimhood repertoire. Both groups also challenged and delegitimised their oppositions' constructions of feminism and victimhood. These findings add to our understanding of the communication strategies used by opposing movements to achieve their mobilization goals.

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

A controlled evaluation of social prescribing on loneliness for adults in Queensland: 8-week outcomes.

Introduction: There have been few controlled evaluations of Social Prescribing (SP), in which link workers support lonely individuals to engage with community-based social activities. This study reports early outcomes of a trial comparing General Practitioner treatment-as-usual (TAU) with TAU combined with Social Prescribing (SP) in adults experiencing loneliness in Queensland. Methods: Participants were 114 individuals who were non-randomly assigned to one of two conditions (SP, n = 63; TAU, n = 51) and assessed at baseline and 8 weeks, on primary outcomes (loneliness, well-being, health service use in past 2 months) and secondary outcomes (social anxiety, psychological distress, social trust). Results: Retention was high (79.4%) in the SP condition. Time × condition interaction effects were found for loneliness and social trust, with improvement observed only in SP participants over the 8-week period. SP participants reported significant improvement on all other outcomes with small-to-moderate effect sizes (ULS-8 loneliness, wellbeing, psychological distress, social anxiety). However, interaction effects did not reach significance. Discussion: Social prescribing effects were small to moderate at the 8-week follow up. Group-based activities are available in communities across Australia, however, further research using well-matched control samples and longer-term follow ups are required to provide robust evidence to support a wider roll out.

Subjective economic inequality evokes interpersonal objectification.

Interpersonal objectification, treating people as tools and neglecting their essential humanness, is a pervasive and enduring phenomenon. Across five studies (N = 1183), we examined whether subjective economic inequality increases objectification through a calculative mindset. Study 1 revealed that the perceptions of economic inequality at the national level and in daily life were positively associated with objectification. Studies 2 and 3 demonstrated a causal relationship between subjective economic inequality and objectification in a fictitious organization and society, respectively. Moreover, the effect was mediated by a calculative mindset (Studies 3-4). In addition, lowering a calculative mindset weakened the effect of subjective inequality on objectification (Study 4). Finally, increased objectification due to subjective inequality further decreased prosociality and enhanced exploitative intentions (Study 5). Taken together, our findings suggest that subjective economic inequality increases objectification, which further causes adverse interpersonal interactions.