Green Alliance

Introduction This article investigates the linguistic and computational challenges of detecting antisemitism in digital communication, integrating discourse-analytical and artificial intelligence (AI) perspectives. It conceptualizes antisemitic discourse as a continuum ranging from explicit incitement to implicit, coded expressions whose interpretation depends on contextual, cultural, and pragmatic knowledge. Methods The study draws on empirical case studies from the Decoding Antisemitism project, analyzing YouTube reactions to two events: the Hamas terror attack of 7 October 2023 and the antisemitic double murder in Washington, D.C., in May 2025. Qualitative discourse analysis is combined with computational considerations related to annotation practices and model design for automated detection. Results The analysis shows that antisemitic discourse has become normalized in mainstream digital spaces. Reactions to 7 October were characterized by open glorification of violence, whereas responses to the Washington case centered on denial, irony, and the inversion of victimhood. Together, these cases illustrate both the normalization and diversification of antisemitic communication online. Discussion Building on these findings, the article discusses methodological and computational implications for antisemitism detection. It highlights challenges such as semantic ambiguity, pragmatic drift, multimodal signaling, and data scarcity, and evaluates emerging computational approaches, including transformer-based fine-tuning, retrieval-augmented systems, and context-engineered large language models (LLMs). The study concludes that effectively confronting digital antisemitism requires sustained collaboration between linguists, data scientists, and policymakers to develop context-sensitive, transparent, and ethically grounded AI systems capable of reliable interpretive reasoning.

To evaluate the equivalence of immunogenicity, safety and efficacy of Gan & Lee (GL) Glargine (Basalin®; Gan & Lee Pharmaceutical) with that of the reference product (Lantus®) in adult participants with type 2 diabetes mellitus. This was a phase 3, multicenter, open-label, equivalence trial conducted across 57 sites. In total, 567 participants with type 2 diabetes mellitus were randomized in a 1:1 ratio to undergo treatment with either GL Glargine or Lantus® for 26 weeks. The primary endpoint was the proportion of participants in each treatment arm who manifested treatment-induced anti-insulin antibodies (AIA). Secondary endpoints included efficacy and safety metrics, changes in glycated haemoglobin levels, and a comparative assessment of adverse events. Results were analysed using an equivalence test comparing the limits of the 90% confidence interval (CI) for treatment-induced AIA development to the prespecified margins. The percentages of participants positive for treatment-induced glycated haemoglobin by week 26 were similar between the GL Glargine (19.2%) and Lantus® (21.3%) treatment groups, with a treatment difference of -2.1 percentage points and a 90% CI (-7.6%, 3.5%) (predefined similarity margins: -10.7%, 10.7%). The difference in glycated haemoglobin was -0.08% (90% CI, -0.23, 0.06). The overall percentage of participants with any treatment-emergent adverse events was similar between the GL Glargine (80.1%) and Lantus® (81.6%) treatment groups. GL Glargine was similar to Lantus® in terms of immunogenicity, efficacy,and safety, based on the current study.

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.