GD POLITICS
This is a free preview of a paid episode. To hear more, visit www.gdpolitics.com [https://www.gdpolitics.com?utm_medium=podcast&utm_campaign=CTA_7] The full episode is available to paid subscribers. Once you become a paid subscriber, you can connect your account to your preferred podcast player here [https://www.gdpolitics.com/listen]. On today’s podcast, Nate Silver joins me to talk about the Maine Senate race, the political fallout of the war in Iran, and much more. Nate had some spicy takes, as you’ll hear. The Maine primary is now completed, and with 80 percent of the expected vote tallied, Platner received 72 percent of the vote. Although Janet Mills had already dropped out of the race, her name remained on the ballot and she received 20 percent. Call it a protest vote. On paper, Maine should be one of Democrats’ best Senate pickup opportunities this cycle. It’s a blue-leaning state in a Democratic-leaning national environment. But after a series of personal controversies, Platner’s campaign has become something more complicated: a test of how much candidate quality and character matter in an era of strong partisanship. Nate makes the case that, for the good of the Democratic Party, Platner should drop out. I play devil’s advocate and ask whether Democrats are likely to rally around him anyway once he becomes inevitable. We also discuss the political risk of a quagmire after the United States’ renewed strikes on Iran in retaliation for a downed Apache helicopter. If the war drags on, and inflation continues to rise, it could shape the midterms more than any one candidate’s scandals. Then we turn to California, where the slow vote count in the Los Angeles mayoral race has once again raised questions about election administration in the country’s largest state. Nate argues that taking this long to count votes is itself a problem, especially when distrust in elections is already so easy to exploit. Lastly, since this is a conversation with Nate, we end with a forecast model. Nate walks through what went into his World Cup forecast: things like national GDP and the total market value of a team’s players, and how similar it is to building a presidential forecast model. We also talk about what building the model taught him about the promise and limits of AI.
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