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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. COMMENTARY · Mastodon — mastodon.social English(EN) ·

    📰 UK Scientists See Little Evidence for Claims Smartphones Are Rewiring Kids' Brains UK's Members of Parliament (MP) were "looking for proof that smartphones an

    A report commissioned by UK Members of Parliament found little scientific evidence to support the claim that smartphones and social media are rewiring children's brains. Neuroscientists consulted for the report indicated that while these devices can impact behavior and mental health, there is no conclusive proof of fundamental neurological rewiring. The findings suggest a need for more robust research to understand the long-term effects of digital technology on developing brains. AI

    IMPACT This report suggests that current evidence does not support widespread claims of direct neurological rewiring due to smartphone use in children.

  2. RESEARCH · arXiv cs.CL English(EN) · · [2 sources]

    Graph-Augmented LLMs for Swiss MP Ideology Prediction

    Researchers have developed a new framework called PG-RAG that enhances Large Language Models (LLMs) for predicting the political ideology of Swiss Members of Parliament. This approach integrates information from political knowledge graphs, capturing both textual data and relationships between MPs, which traditional LLMs often overlook. The study demonstrated that augmenting LLMs with this graph-structured information improves prediction accuracy compared to existing methods. AI

    Graph-Augmented LLMs for Swiss MP Ideology Prediction

    IMPACT This research highlights the potential of integrating relational data with LLMs for nuanced analysis in social sciences.

  3. RESEARCH · arXiv cs.CL English(EN) ·

    Supercharging Agenda Setting Research: The ParlaCAP Dataset of 28 European Parliaments and a Scalable Multilingual LLM-Based Classification

    Researchers have developed ParlaCAP, a new dataset designed to analyze parliamentary agenda setting across 28 European countries. This dataset utilizes a multilingual LLM in a teacher-student framework to create domain-specific policy topic classifiers, achieving annotation agreement comparable to human annotators. The ParlaCAP dataset includes extensive metadata and sentiment predictions, enabling comparative research on political attention and representation. AI

    Supercharging Agenda Setting Research: The ParlaCAP Dataset of 28 European Parliaments and a Scalable Multilingual LLM-Based Classification

    IMPACT Enables new avenues for comparative political science research using LLM-based analysis of parliamentary data.