Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are trusted news sources, viewers are particularly susceptible to the information they report. The Sinclair Broadcast group is a broadcasting company that has acquired many local news stations in the last decade. We investigate the effects of local news stations being acquired by Sinclair: how does coverage change? We use computational methods to investigate changes in internet content put out by local news stations before and after being acquired by Sinclair and in comparison to national news outlets. We find that there is clear evidence that local news stations report more frequently on national news at the expense of local topics, and that their coverage of polarizing national topics increases.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

Random forests are often an accurate method for nonparametric regression but they are notoriously difficult to interpret. Also, it is difficult to construct standard errors, confidence intervals and meaningful measures of variable importance. In this paper, we construct a spatially adaptive local linear smoother that approximates the forest. Our approach builds on the ideas in Bloniarz et al. (2016) and Friedberg et al. (2020). The main difference is that we define a one parameter family of bandwidth matrices which help with the construction of confidence intervals, and measures of variable importance. Our starting point is the well-known fact that a random forest can be regarded as a type of kernel smoother (Breiman (2000); Scornet (2016); Lin and Jeon (2006); Geurts et al. (2006); Hothorn et al. (2004); Meinshausen (2006)). We take it as a given that the forest is an accurate predictor and we do not make any attempt to improve the method. Instead, we want to find a family of linear smoothers that approximate the forest. Then we show how to use this family for interpretation, bias correction, confidence intervals, variable importance and for exploring the structure of the forest.

When most people think of the connection between technology and jobs, they think of robots and automation taking over relatively unskilled jobs like factory work. And thus, the biggest toll from these technological advances would be on already hard-hit manufacturing regions of the Rust Belt. But a new wave of developments in artificial intelligence may have a greater effect on high-skilled jobs and high-tech knowledge regions. The study by Mark Muro, Jacob Whiton, and Robert Maxim takes a close look at the potential of artificial intelligence--or AI--to automate tasks that until now have required human intelligence and decision-making. As they put it: "Unlike robotics (associated with the factory floor) and computers (associated with routine office activities), AI has a distinctly white-collar bent."

It's long been clear that urbanization and automated technologies are shaping society, but it hasn't been obvious how the two forces affect each other. A new study from MIT's Media Lab posits that the smaller the city, the greater the impact it faces from automation. The finding, they say, could encourage legislators to pay special attention to workers in smaller cities and offer them support services. Other researchers have attempted to measure the effect of technology on employment in cities, but the Media Lab authors, who have identified which jobs and skills tend to be more prevalent in smaller cities and larger ones, claim to be the first to explain why different U.S. cities are more susceptible (or resilient) to technological unemployment. They say that bigger cities have a disproportionately large number of jobs for people who do cognitive and analytical tasks, such as software developers and financial analysts--occupations that are less likely to be disrupted by automation.

Where are the robots, exactly? One answer--if you read the steady flow of doomy articles online -- is that automation is everywhere, not just all over the media but (you would have to conclude) thoroughly infiltrating the economy. In that sense, the trend seems omnipresent even as it spawns a kind of free-floating dread amongst the chattering class. Yet, that can't be right. Almost nothing in today's economy is evenly distributed, whether it be technology, productivity, output, or inclusive prosperity.

Imagine this: your hospital administrator asks you to help reduce the length of inpatient stays and they need a plan within a week. Chances are, most of you couldn't. But, the technology to mine and analyze your data does exist. Much like your daily Google searches, it's possible to input your search criteria, click Enter, and have answers at your fingertips in seconds. Doing so is part of radiology's push toward using big data, said Woojin Kim, MD, director of innovation at Montage Healthcare Solutions, Inc. "Radiology doesn't yet have big data like other industries, but that's changing rapidly. People want access to data to be able to turn insight into action," he said.

Imagine this: your hospital administrator asks you to help reduce the length of inpatient stays and they need a plan within a week. Chances are, most of you couldn't. But, the technology to mine and analyze your data does exist. Much like your daily Google searches, it's possible to input your search criteria, click Enter, and have answers at your fingertips in seconds. Doing so is part of radiology's push toward using big data, said Woojin Kim, MD, director of innovation at Montage Healthcare Solutions, Inc. "Radiology doesn't yet have big data like other industries, but that's changing rapidly. People want access to data to be able to turn insight into action," he said.