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Humanity has already exceeded Earth’s limits, study warns
Humanity may already be living far beyond what Earth can sustainably support, according to a sweeping new study analyzing more than 200 years of population and environmental data. Researchers found that while population growth once fueled innovation and expansion, the trend shifted decades ago as the planet’s resources became increasingly strained.
Analyzing Chris Rabb's win in Pennsylvania's 3rd District Democratic primary
A political expert and a Philly labor leader discuss Chris Rabb's win in Pennsylvania's 3rd District Democratic primary.
AI scans 400,000 Reddit posts and finds hidden Ozempic side effects
By analyzing over 400,000 Reddit posts, researchers discovered that users of popular GLP-1 weight-loss drugs frequently discussed unexpected symptoms like menstrual irregularities, chills, and hot flashes. The findings suggest AI could turn social media into a powerful early-warning system for spotting side effects that clinical trials may miss.
Penn Entertainment CFO Felicia Hendrix talks career change from Wall Street to gaming industry
Felicia Hendrix traded in a career on Wall Street analyzing the gaming industry to work in it at a high level. The executive vice president and chief financial
N.Y. state Court of Appeals hears social media hateful conduct case
The courts are weighing and analyzing the constitutionality behind reporting and enforcing hateful conduct online.
Where do citizen science data come from?
Citizen science projects are playing an increasingly important role in ecological and conservation research worldwide. In these programs, members of the public support researchers by submitting photographs, audio recordings, observations, or even by contributing to data analysis. In Hungary, no fewer than 17 such initiatives combined their datasets in a joint study. The authors aimed to understand which socio-economic and environmental factors influence participation patterns in Hungary. By comparing the spatial distribution of citizen science data with an independent administrative dataset (municipality level data on income, education, demography, density and protected areas), the researchers identified several general trends as well as numerous project-specific relationships. For example, they found more per capita observations from municipalities with higher proportions of protected natural areas and lower population densities. Furthermore, excluding Budapest, the capital, both the proportion of people with diplomas and the proportion of elderly people showed a positive correlation with participation activity. In contrast, no consistent general pattern emerged regarding wealth or the proportion of children, although several project-specific relationships were observed. The study not only reveals how citizen science data are distributed across Hungary, but also provides robust support for the view that various biases must be taken into account when analyzing such data. The authors hope that their findings will help researchers in other countries design more effective projects and produce more accurate analyses by accounting for some of these or similar biases.
Scientists use DNA from poop to save the world’s rarest marsupial
Scientists in Australia are using cutting-edge DNA techniques to help save one of the world’s rarest marsupials — the critically endangered Gilbert’s potoroo, with fewer than 150 left in the wild. By analyzing tiny traces of DNA in the animals’ scat, researchers uncovered clues about the elusive fungi the potoroos depend on for survival. The findings could help conservationists identify safer new habitats and establish backup populations before disasters like bushfires wipe them out.
Whoa! Netflix is Sending Narnia Heading to Theaters...What This Means for the Future of Film
(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) Here’s a funny thing about the debate over whether streamers should release their movies in theaters first: Both sides think the other side represents the “conventional wisdom”. If you’ve been reading me for any amount of time, or even just scanned my front page, you know I think movies make more money by having a robust theatrical window compared to going straight-to-streaming. I’ve published thousands of words on this topic. And for years, I felt like I was standing up to a wave of opposition. See, while the “put your films in theaters to make money” take was obvious at any point before 2019, post-streaming revolution, most of the trendy thinkers took the opposite point of view. Big names argued that Netflix putting their film straight-to-streaming (“S-to-S” from here on out) actually made more money. Netflix was “disrupting” theaters, just as they disrupted Blockbuster Video, and since innovation is always good, this too was a great idea. This thinking then leaked into the mainstream business outlets like the Wall Street Journal and Bloomberg. I read many articles whose take boiled down to “Netflix makes tons of money from S-to-S releases” and “theaters were in structural decline” anyways. Covid-19 shutting down theaters and the 2023 strikes shutting down film production seemingly justified this point of view, as the box office struggled to return to 2017-2019 heights. Yet I’ve also read folks arguing the opposite point: the conventional wisdom says to put films in theaters! From some writers’ points of view, “everyone” actually agrees/agreed with my “send your films to theaters” take—especially the stodgy traditional studios—and folks still didn’t understand Netflix’s genius. I feel like, for the last two years, many people have argued this point as if it’s conventional wisdom. You know what? I may have been wrong. At this point, the “S-to-S strategy makes money” might be the minority viewpoint. Especially with the news this month that Netflix will put at least one film—Greta Gerwig’s Narnia film The Magician’s Nephew—in theaters in 2027 for a full 49-day run is big news. So that’s the story of the week, but I’ll also explain why they’re making this move now and why Netflix may not completely embrace theaters, along with other topics, like how I don’t buy Disney’s “super app” strategy, the latest good news out of the unions, my take on the latest attacks against free speech, and a whole lot more. Let’s get started. Subscribe Most Important Story of the Week - Netflix Goes to Theaters! Listen, I promise not to victory lap too hard today. As I just wrote, I’ve argued against sending films straight-to-streaming for—checks calendar—eight years now. Since 2018. Yikes. That’s a long time. And for years, others painted my thinking as “conventional” in that I was standing up for the status quo against trendy, cool disruption. And that’s not cool! But math is math, and I never could make the S-to-S numbers work. The only thing that ever enabled that model was Wall Street’s brief exuberance for it. It seems like the heads of Netflix now agree with me. (There were always rumors that former head of film for Netflix Scott Stuber argued internally for theatrical releases as well.) As they announced: “The Magician’s Nephew_—the first film in a Narnia series of films—will open on 12-Feb-2027, with a 49 day theatrical exclusive run.”_ I’m a little surprised at how little coverage this big change actually got. Credit to Bloomberg for leading with it and Matt Belloni’s The Town for highlighting it with the provocatively titled podcast “Hell Hath Frozen Over”. But after that? Instead of getting as much coverage as, say, the FCC’s latest egregious crackdown on free speech, this news story got crickets. But it’s massively more important for the future business of entertainment. So let’s start with the obvious question… Why Put Your Movies in Theaters? So…why should Netflix put (some!) of their movies in theaters? Well, I’ve already written that in a very, very, very long article from 2022: [ ](https://entertainment.substack.com/p/the-data-is-in-theatrical-films-massively) [ The Data Is In: Theatrical Films Massively Outperform Straight-To-Streaming Films ](https://entertainment.substack.com/p/the-data-is-in-theatrical-films-massively) Entertainment Strategy Guy · April 5, 2023 [ Read full story ](https://entertainment.substack.com/p/the-data-is-in-theatrical-films-massively) To summarize, theatrical runs make much more money, especially blockbusters, because they earn more money per viewing,1 and that helps boost home entertainment sales. Theatrical films also perform as well or better than the streaming-only films. Now, partially this is driven by increased marketing for said films, but said marketing ultimately boosts the awareness of the movies and drives improved streaming and library performance as well. Again, I’ve made this case after reviewing the economic models and streaming data for years. Just last week, Emily Horgan also made the case for kids films! Yes, it is probably conventional wisdom, but that’s also because it’s true! I’d also argue we’ve seen a lot of data in the various studios’ behavior. As I’ve noted before, the more a studio needs to make money, the likelier they are to put films in theaters. As such, Disney, Universal, Warner Bros., Paramount and Sony all send their major films to theaters. Even if they once made a big S-to-S push (*cough* Disney *cough*), they’ve since relented and returned to theaters. The tech companies don’t need to make money, per se, so it took them years to embrace theaters. Apple doesn’t need to make money because it doesn’t disclose their (likely) Quibi-sized losses each year. (They fear bad press more than losing money.) Amazon announced two years ago that they planned to make Amazon-MGM Studios/Prime Video profitable on its own, and guess what? Now they’re putting films in theaters. That leaves Netflix. They’re in between the “no need to make money at all” and the “desperately need to make money to offset linear TV declines”, so they had less pressure to put some films in theaters. Plus, for years, they were the sexy disruptor, and Wall Street rewarded them for that. (Honestly, they still do.) Yet, even Netflix likely saw the money they were leaving on the table (I’ve estimated it at an easy billion dollars before) so they changed their course. I heard one pundit argue that, because Netflix already makes so much money, they don’t need to make more money, which sort of goes against everything I’ve ever learned about business and economics. Again, to be super-nuanced, sending “films to theaters” does not mean EVERY film. TV movies have been a thing for decades. Lifetime films, Hallmark holiday films, Disney Channel Original Movies, and so on, don’t need theatrical runs. But films above a certain budget level (say $25 to 50 million) almost certainly need theaters to maximize their revenue. The Scenarios (with a Dose of Skepticism) Now time for some skepticism: it won’t surprise me at all if Netflix re-changes course. Specifically, Netflix announced that their Narnia film will go to theaters. And I’d bet anything we see KPop Demon Hunters 2: The Huntering2 also goes to theaters, cause it’s the surest hit outside of a Disney animated sequel right now. But how much does Netflix truly commit to theaters? They’ve already tried to emphasize that this is a one-off move. I see three broad scenarios:
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
New study debunks the biggest fear about yo-yo dieting
For years, “yo-yo dieting” has been blamed for wrecking metabolism and causing lasting damage, but a major new review says the fear may be wildly overblown. After analyzing decades of studies in humans and animals, researchers found little convincing evidence that losing weight and regaining it actually causes long-term harm. While regaining weight can erase some health improvements, it doesn’t appear to make people worse off than before.
Analyzing potential Mets starting pitching replacements following Clay Holmes injury
Colon cancer is rising in young adults and doctors don’t fully know why
Colorectal cancer is increasingly showing up in younger adults, with cases now appearing in people as young as their thirties — often with no family history or warning signs. A major Swiss study analyzing nearly 100,000 cases over four decades found that diagnoses in people under 50 have been steadily climbing, even as rates fall among older adults thanks to screening programs. Researchers say younger patients are also more likely to be diagnosed late, after the cancer has already spread.
Giant squid discovery uncovers a hidden deep-sea world off Australia
Scientists exploring deep underwater canyons off the coast of Western Australia uncovered a hidden world packed with bizarre and elusive marine life — including signs of the legendary giant squid. By analyzing traces of DNA floating in seawater from depths exceeding 4 kilometers, researchers identified 226 species ranging from deep-diving whales to strange fish rarely or never seen in the region before. Some of the creatures may even be unknown to science.
Who are the Japanese? Huge DNA discovery rewrites history
Scientists analyzing the genomes of thousands of people across Japan discovered evidence for a previously overlooked third ancestral group, challenging the long-accepted “dual origins” theory. The newly identified ancestry appears linked to the ancient Emishi people of northeastern Japan. Researchers also uncovered inherited Neanderthal and Denisovan DNA connected to conditions like diabetes, heart disease, and cancer.
James Webb telescope reveals the clearest map ever of the Universe’s cosmic web
Astronomers using NASA’s James Webb Space Telescope have created the clearest map yet of the universe’s “cosmic web” — the enormous hidden structure that connects galaxies across space. By analyzing more than 164,000 galaxies through the massive COSMOS-Web survey, researchers were able to trace this vast network back to when the universe was just a billion years old.
The Universe’s biggest black holes may be forged in violent mergers
The Universe’s biggest black holes may not be born giants after all. Scientists analyzing gravitational-wave signals from dozens of black hole collisions found evidence that the heaviest black holes are likely “cosmic recyclers” — formed through repeated smashups inside incredibly crowded star clusters. These violent chain reactions appear to create a distinct class of rapidly spinning black holes that stand apart from ordinary ones formed by dying stars.
Brain scans reveal 3 ADHD subtypes, including a more extreme form
A study analyzing 1,154 children and adolescents breaks new ground in how to think about the growing diagnosis.
Spring practice ends without answers as Alabama, Florida, Tennessee QB races intensify
How this SEC trio is analyzing each of the QB1 candidates following the spring
2026 NFL Draft Grades: Analyzing All 32 Teams' Classes
Every NFL team just added several players to their rosters, but who really got better? Rob Rang provides his post-draft grade for each club.
DNA research just rewrote the origin of human species
Scientists have uncovered a surprising new picture of human origins that challenges the long-held idea of a single ancestral population in Africa. By analyzing genetic data from diverse modern African groups—especially the highly distinct Nama people—and comparing it with fossil evidence, researchers found that early humans likely evolved from multiple intermingling populations over hundreds of thousands of years. Rather than a clean split, these groups stayed connected, exchanging genes even after beginning to diverge around 120,000–135,000 years ago.
Sales include complex for care facility, new equipment rental business
A multi-building complex that is leased by The Resource Center is under new ownership. The Post-Journal and OBSERVER have been analyzing real estate sales in Chautauqua County and recently looked at the transactions from Feb. 11-17. The county does not provide transaction information, but real estate documents are available for review at the clerk’s office.…
Scientists just uncovered a 3 million-year climate mystery in Antarctic ice
Ancient Antarctic ice is revealing a surprising new chapter in Earth’s climate story, stretching back 3 million years. By analyzing tiny pockets of trapped air and rare gases, scientists have discovered that while the planet cooled significantly—especially in the oceans—levels of key greenhouse gases like carbon dioxide and methane changed only modestly. This unexpected mismatch suggests other powerful forces, such as shifting ice sheets, ocean circulation, and Earth’s reflectivity, played major roles in driving long-term climate change.
For the first time, scientists pinpoint the brain cells behind depression
Scientists have identified two specific types of brain cells that behave differently in people with depression, offering a clearer picture of what is happening inside the brain. By analyzing donated brain tissue with advanced genetic tools, the researchers found changes in neurons linked to mood and stress, as well as in immune-related microglia cells. These differences point to disruptions in key brain systems and reinforce that depression is rooted in biology, not just emotions.
Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
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This transformation enables WellSpan to unify disparate data systems for care teams and patients alike, achieving operational efficiencies and care improvements that fragmented systems are unable to deliver. “Our comprehensive migration to the cloud accelerates our ability to deploy new clinical capabilities that transform patient care while advancing our overall innovation strategy,” said Roxanna Gapstur, Ph.D., R.N., president and CEO, WellSpan Health. “When a physician can access complete patient imaging in seconds instead of hours, we're not just modernizing technology—we're improving patient outcomes. Our new cloud infrastructure also enables us to quickly implement solutions that will free our clinicians from thousands of hours of administrative work annually so they can focus on what they do best: caring for our communities.” The transition allows WellSpan to move away from a data center-based model and enables the rapid deployment and scale of AI technology. WellSpan’s strategic partner, General Catalyst’s Health Assurance Transformation Company (HATCo), facilitated the collaboration with AWS to support the migration. WellSpan’s transformation journey benefits from HATCo’s specialized technology solutions, as well as General Catalyst and AWS’s significant partnership to improve patient outcomes, which was announced in January 2025. “Our decision to go all-in on AWS will enable insights and innovations impossible in traditional siloed environments,” said Kasey Paulus, executive vice president and chief operating officer, WellSpan Health. “This is the largest health care technology transformation in our region's history, and it positions WellSpan to be a premier partner for technology leaders across the country.” “WellSpan is building a foundation with AWS that will improve care and outcomes for patients across their health system by bringing data together into standardized formats that are optimized for AI applications,” said Dan Sheeran, vice president and general manager, Healthcare and Life Science, AWS. “To do this, it is essential to have the most secure and compliant cloud when handling patient data, along with AWS’s built-in AI governance and responsible AI frameworks that follow the industry’s highest standards to protect patient information. We look forward to continuing to work alongside WellSpan as they unlock the cloud and AI’s full potential to deliver exceptional care to their patients.” Key Benefits of the AWS Collaboration Full Portfolio Migration: WellSpan will transition its applications and data from on-premises data centers to AWS, including full IT environments and specialized applications for functions such as nursing, cardiology, pharmacy and the company’s supply chain systems. This represents approximately 7.5 petabytes of clinical and non-clinical data and more than 300 applications—the most comprehensive health system cloud migration in the Mid-Atlantic region. Approved Architectures: AWS will deliver secure, WellSpan-specific architectures tailored to each build, ensuring compliance and reliability across the health system. Unified, AI-Ready Data: Migrating to AWS enables WellSpan to harmonize data across separate systems, transforming unstructured clinical notes and multi-modal datasets into standardized, AI-ready formats. Accelerated Results and Improved Outcomes: Automated data normalization and processing will enable the deployment of applications that reduce turnaround times for items such as analyzing medical imaging or patient records from days to minutes, helping clinicians get patient results faster and improving overall health outcomes. Reduced Administrative Burden: The transition to AWS will enable swift deployment of AI-powered solutions that reclaim thousands of hours for WellSpan’s clinical and corporate teams, allowing them to focus on patient care rather than paperwork. Elastic Infrastructure: Cloud architecture will enable WellSpan to scale computing resources dynamically, supporting seasonal demand fluctuations without infrastructure investments, as well as the rapid deployment of new services across multiple locations simultaneously. Most Secure, Compliant Cloud: AWS offers the highest network availability of any cloud provider, with more than 166 HIPAA-eligible services and certifications for global compliance standards, including HITRUST CSF. Built-in AI governance and responsible AI frameworks ensure that WellSpan’s data is protected under the industry’s most rigorous standards. Tags:News releaseInnovation and partnerships
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