Predicting the replicability of social and behavioural science claims in COVID-19 preprints.

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources.

Generative language models exhibit social identity biases.

Social identity biases, particularly the tendency to favor one's own group (ingroup solidarity) and derogate other groups (outgroup hostility), are deeply rooted in human psychology and social behavior. However, it is unknown if such biases are also present in artificial intelligence systems. Here we show that large language models (LLMs) exhibit patterns of social identity bias, similarly to humans.

Why misinformation must not be ignored.

Recent academic debate has seen the emergence of the claim that misinformation is not a significant societal problem. We argue that the arguments used to support this minimizing position are flawed, particularly if interpreted (e.g.

Strong coupling between WS monolayer excitons and a hybrid plasmon polariton at room temperature.

Light-matter interactions between plasmonic and excitonic modes have attracted considerable interest in recent years. A major challenge in achieving strong coupling is the identification of suitable metallic nanostructures that combine tight field confinement with sufficiently low losses. Here, we report on a room-temperature study on the interaction of tungsten disulfide (WS) monolayer excitons with a hybrid plasmon polariton (HPP) mode supported by nanogroove grating structures milled into single-crystalline silver flakes.