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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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Viewpoint: FP10 should remain open, excellent and secure
Europe is right to worry about research security. Foreign interference is real. So is the misuse of sensitive knowledge. Universities working in sensitive areas such as AI, quantum, semiconductors and advanced materials cannot afford to ignore the geopolitical environment around them.But Europe is in danger of drawing the wrong conclusion, one that would increase, not reduce, its strategic vulnerabilities.If the EU applies an “EU preference” logic to the next Framework Programme for research and innovation (FP10), it will weaken one of its greatest strategic assets: a research system built on openness, excellence and international collaboration. In the name of security, Europe could end up advancing science more slowly, innovating less effectively and making itself less attractive to global talent and partners in advanced science and technology.That would be a serious mistake.The EU preference included in Article 10 of the proposed European Competitiveness Fund may make sense in some industrial or deployment contexts, but is far harder to justify when applied to collaborative research in advanced science and technology, where excellence and global connectivity are crucial. Research is not procurement. It does not thrive when isolated. It advances through access to the best ideas, the best people, the best facilities and the best data, wherever they are found. This is why FP10 should not simply absorb industrial policy logic by default. Doing so would risk importing instruments that are ill-suited to research and that ultimately increase vulnerability rather than resilience.For universities of science and technology, the issue is especially acute. These institutions sit at the frontier of knowledge and technology creation in the very fields Europe sees as critical for its future competitiveness and resilience. They know better than most that security risks are real. But they also know that openness and security are not opposites. In practice, they have to be made to work together. Research security is not about closing doors. It is about building resilience through collective responsibility, trust, proportionate safeguards and better governance. That is a fundamentally different approach from default restriction.The risks of non-collaborationThe real danger in the current debate is that Europe talks too much about the risks of collaboration and not enough about the risks of non-collaboration. Those risks are strategic too. Cut researchers off from international networks and Europe loses access to knowledge, data, infrastructures and talent. Slow down global collaboration in critical technologies and Europe slows down its own progress on cutting-edge science. Make participation rules unpredictable and Europe weakens its attractiveness to researchers and investors alike.In other words, over-restricting collaboration will create strategic vulnerabilities, particularly in advanced science and technologies.This point is often underestimated in policy discussions that frame openness mainly as a value to be defended. Europe’s scientific strength, technological development and long-term competitiveness depend on remaining connected to the best research ecosystems in the world. For a continent that wants to accelerate deep tech, strengthen industrial capacity and reduce strategic dependencies, weakening the excellence base would be self-defeating. That does not mean ignoring risk. Quite the opposite. Universities are already strengthening due diligence, internal governance, awareness-raising, screening procedures and compliance support. Across Europe, institutions are working to manage risks linked to foreign interference, sanctions, export controls and the misuse of research. The right response is not complacency. It is better risk management. But that means targeted, evidence-based and proportionate measures, not blunt top-down restrictions with wide spillover effects. In the daily reality of universities, openness and restriction often coexist. The real challenge is how to apply safeguards without sliding into securitisation that damages academic freedom, open science and international cooperation. Restrict partners, not nationsMore restrictive treatment of collaborators from European Economic Area members Iceland, Liechtenstein and Norway, or from Switzerland and the UK, is strategically very difficult to justify. These are deeply embedded partners in Europe’s research and innovation ecosystem. Beyond that, Europe should still resist default exclusion and instead rely on case-by-case assessment. The relevant question is not nationality in the abstract. It is the actual risk profile of a collaboration: the institution, the affiliations, the field, the knowledge involved, the intended use and the safeguards available. Screening should focus on institutions and affiliations rather than nationality alone. Related articlesParliament ‘misreads’ link between Horizon Europe and ECF, Commission official saysFrustration grows at governance gap in next Horizon EuropeIf EU preference logic is imported into research without careful calibration, the likely result is a chilling effect. Researchers may step back from legitimate and beneficial collaboration, even where risks are manageable. International partners may see Europe as less predictable. And Europe may end up undermining the very excellence base it needs to compete, scale up and lead in strategic technologies, thereby increasing its dependence on others.FP10 should do the opposite. It should remain open by default, with restrictions used only exceptionally, and then on the basis of clear evidence. It should integrate research security into open science and research integrity frameworks, not set them against each other. And it should recognise that academic freedom, scientific excellence and responsible international collaboration are not obstacles to Europe’s security, they are part of it. Europe needs stronger tools to protect itself. But it also needs the discipline to avoid measures that would weaken it and increase its strategic vulnerabilities in the process. In FP10, that means staying open, excellent and secure.Louise Drogoul is senior advisor for innovation and sustainability at the Cesaer university association.
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The Boundless Deep: Young Tennyson, Science, and the Crisis of Belief by Richard Holmes
Pantheon Review by Walter Cummins The image of Alfred Tennyson I’ve carried for decades goes back to the childhood card game of Authors that depicts him with a stately continence sharpened by a jutting black beard. He’s formidable, his aura superior. Holmes opens his biography of the youthful Tennyson by describing the similar formality of the imposing Tennyson statue at Trinty College Cambridge, sharing the honor with other Trinity greats, including Isaac Newton, Francis Bacon, and Thomas Babington Macaulay. Holmes doesn’t want to chip away at that statue but rather reveal its inner youthful core of the man—tall, handsome, and talented but unformed, emerging from a disturbed family of addicted or insane male siblings and four unusual sisters struggling under a violent alcoholic clergyman father. Alfred was certainly statuesque, but would he end up a block of unformed stone like his equally promising brothers? He was youngest of three equally regarded Tennyson brothers at Cambridge, all bright, handsome, and talented, in fact Alfred regarded by some as less promising than his elders who also wrote poetry. Yet Alfred wrote one of his major poems, “The Kraken,” at twenty, revealing both his talent, his interest in geological forces, and the haunting creature that embodied his anxieties. While Holmes focuses on Tennyson of the 1830s and 1840s, he can’t tell his story without exploring his involvement with the Victorian intellectual upheaval. Tennyson was fascinated by new scientific perspectives, following the latest findings as soon as books and articles were published. He was especially taken with Charles Lyell’s deep studies of geology and the discovery of fossils and a timeline of the planet’s development that did much to question fundamental religious assumptions. The impact of these discoveries is reflected in Tennyson’s poetry and led to the crisis in belief he shared with so many contemporaries. He was especially taken by the cycles of destruction and renewal that informed the struggles he confronted in In Memoriam, Maud, and Idylls of the King. Tennyson was only one of the eventual major figures at Cambridge at the same time, young men just beginning to realize their potential. Charles Darwin was at Christ College, although there is no evidence that he and Tennyson even met as students or later in life. Yet evolution became significant in Tennyson’s thinking and the issues of his poetry. One of Tennyson’s best and most important Cambridge friends was Edward FitzGerald, who became the translator of The Rubáiyát of Omar Khayyám and in their on and off relationship served as Tennyson’s harshest critic. FitzGerald was also a friend of the novelist William Makepeace Thackeray, the two of them partying in Paris as undergraduates. That connection led to a friendship between Thackeray and Tennyson. Others who came to play significant roles in Tennyson’s life were Thomas Carlyle, the Brownings, and the Pre-Raphaelites. Tennyson’s deepest friend at college was Arthur Hallam, who at twenty wrote a glowing review of Tennyson’s first poetry collection, travelled on European adventures with him, and fell in love with his sister Emily. Hallam was also regarded as one more very promising Cambridge student. His sudden death at twenty-two in Vienna from a stroke while travelling with his father devastated many friends, but perhaps Tennyson most of all. He did not write immediate poems of grief but took years to compile those collected in In Memoriam A.H.H., published in 1850, almost twenty years after Hallam’s death For all of Tennyson’s early poetic successes and successes, Holmes reveals him as an often troubled young man suffering periods of depression, fearing planetary upheaval, concerned about supporting himself, disappointed in romances, and worried that he would inherit the family’s mental illness tendencies. Yet despite his seeming withdrawals, he was always writing poetry that he kept private and would not let go, constantly revising. 1850 became a transformative year for Tennyson. He finally consented to publishing In Memoriam with a last-minute prologue that acknowledged God and perhaps convinced the religious Emily Sellwood to marry him after years of pursuit.. He opened up to Emily, revealing his struggles with fears of depression and “weird seizures.” The devastating event of that time was the stillborn death of his unnamed first son. It shook him, but Emily’s faith brought them closer and convinced him to believe in an afterlife. Two later sons survived. With the death of William Wordworth in April of 1950, Tennyson was named poet laureate at age forty-one, reluctantly accepting. It’s as if Tennyson’s life split in half in that year. He lived another forty years, now a recognized success, with the prestige of being an official poet writing patriotic verse, wealthy and revered, no longer the insecure young man seeking an outlet for his immense talent. He earned a place in the Authors deck, now dignified and in 1884 elevated to the peerage as Arthur, Lord Tennyson. Richard Holmes is the author of twenty previous books on the Romantics and Victorians, several of which received awards. The Boundless Deep was shortlisted for the 2025 Baillie Gifford Prize for Non-Fiction. He captures the complex vicissitudes of Tennyson’s youth, illustrated by frequent excerpts from the poetry, placing that life in the full context of the shattering discoveries that led to an upheaval of thought in the Victorian age.
A New Model to Drive Financial Health and Commercial Sustainability in the Development Sector - SPONSOR CONTENT FROM MASTERCARD
Sponsor content from Mastercard.
Immunic COO highlights the importance of science-led innovation to mark World Health Day
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Ella Duggan Promoted to Branch & Business Development Manager at The Merrimack
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ING terminates sale agreement for Russian business
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Upcoming Tech and Sourcing Webinar on Digital Health
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Spanberger signs bipartisan bills tied to billions in business investment across Virginia
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ODOD invests several thousand into business expansion
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Investing in Tomorrow: Strengthening Human Development and Resilience in Ukraine
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AWS Engineer Reports PostgreSQL Performance Halved By Linux 7.0, But A Fix May Not Be Easy
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MassLive: Boston, Springfield land on top 10 list of best cities for STEM professionals
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Is GTA 6's Long Development The Result Of A Complete Engine Rebuild? It's Probably More Complicated Than That
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What to know about the Kansas sports authority that will oversee the new Chiefs stadium
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Flutter Entertainment NYSE FLUT Valuation Check After FanDuel Predicts Optimism And Favorable Sportsbook Results
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Iterative Health, US Heart & Vascular Partner to Advance Community-Based Cardiovascular Research
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FTC Announces New Health Care Task Force
In a development likely to ramp up regulatory pressure on an industry already under significant federal scrutiny, Federal Trade Commission (FTC) Chairman Andrew Ferguson recently directed leaders…
New Electronics Manufacturing Business Comes to Alamosa County with Support from Rural Jump-Start Program - by Ark Valley Voice Staff
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Santa Clara businesses sue over shopping center displacement
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Researchers lead push for health innovation at NextGen Pathways Symposium
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A new way to detect breakthroughs in science: Large-scale analysis reveals 'disruptive' innovations in research history
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Call for Applications: APSA Diversity and Inclusion Advancing Research Grant for Early-Career Scholars | Deadline: May 3, 2026 -
The APSA Diversity and Inclusion Advancing Research Grant supports the advancement of research goals and professional development of early-career political science scholars. The grant will award scholars whose research areas focus on one of the [...]
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Politics Keeps D.C.’s Autonomous Vehicles Roadblocked | The Foundation for American Innovation
Autonomous vehicles (AVs), in development for years, have only recently become safe enough for mass adoption. Now, events like Uber’s new partnerships with AV companies
Pennsylvania Lawmakers Are Talking the Talk on Data Center Regulations. But Will They Walk?
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Stacy LaiFook to lead Annuity Business Development for Nationwide
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Tri-Cities business leaders worry there’s not enough power to go around
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'Starting a Business in Arkansas' workshop slated in Paragould - Talk Business & Politics
Anyone interested in starting a business in Arkansas is invited to attend a free, in-person workshop, “Starting a Business in Arkansas,” hosted by the Arkansas Small Business and Technology Development Center...
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EXCLUSIVE: Eidos Montreal Cancels 7+ Year In-Development Title That Cost Hundreds of Millions
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‘Give me another solution.’ Counties resist mental health centers, betraying patients and families
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Brandi Gabbard sets kickoff as St. Petersburg mayoral candidates build early war chests
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Assessing WOTC and EZEC Eligibility for Professional Employer Organizations: A Review of Barrett Business Services, Inc. v. Commissioner — Current Federal Tax Developments
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Shawnee proposes TIF district to help businesses near Midland Drive, I-435 impacted by construction
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Quilty Launches as the Entertainment's Intelligence Platform
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