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Four Young Mathematicians Receive Prestigious Fields Medals
The awards are bestowed only once every four years to mathematicians under the age of 40.
John Pardon Untangles the Theoretical Loops
The mathematics professor has won a prestigious Fields Medal for his work at the intersection of geometry, topology and physics.
Yu Deng Predicts the Actions of the Whole From Its Tiniest Parts
Dr. Deng, a mathematics professor at the University of Chicago, received a Fields Medal for his work on an equation describing fluid mechanics.
2nd flood in a week in Gallup damages businesses
GALLUP, N.M. (KRQE) – Residents in Gallup are cleaning up the aftermath left by floods covering entire streets. City leaders said they haven’t seen a storm like this in years. Those waters ha…
When a SpaceX rocket crashes into the moon, scientists will be watching
On Aug. 5, 2026, an abandoned piece of space junk is predicted to crash into the moon near a spot called Einstein Crater. Astronomers will be watching because the collision and aftermath may be visible ...
'A very infectious person'
Keegan - who died on Monday - signed Mathie for Newcastle in 1993. Mathie told BBC Radio Suffolk's The Blue Hour: "Very sad, really very sad. They've lost a football great there as a player, a manager and a person.
Nintendo says it has no legal duty to pass its U.S. tariff refunds on to consumers
Nintendo is asking a court to dismiss a proposed class action suit that was filed this past spring by gamers who say the Mario maker is breaking the law if it doesn’t pass on its U.S. tariff refunds to consumers. Those consumers “received exactly what they bargained and paid for” when buying price-hiked Nintendo goods last year, the company said in a filing on Monday in the case of Hoffert et al v. Nintendo. “Plaintiffs are not entitled to a rebate simply because of intervening legal developments related to tariffs.” Nintendo increased the price of Switch 2 controllers and the original Switch last year, following the imposition of globe-spanning U.S. tariffs (tariffs described by Nintendo’s lawyers on Monday as “sweeping” and “constantly changing”). This past February, the U.S. Supreme Court ruled that the tariffs were illegal. A month later, Nintendo sued the U.S. government to get a refund. And in April, gamers filed the class action claim against Nintendo in Washington, saying it would be illegally double-dipping if it made money from tariff-hiked sales and then from a refund. The gamers sought relief for anyone in the U.S. who bought price-hiked Nintendo items between February 2025 and February 2026. “The common thread among Plaintiffs’ claims is that it is somehow ‘unfair; that Nintendo has not retroactively adjusted its prices for completed sales in response to the outcome of the tariff litigation,” the company’s lawyers wrote in today’s motion to dismiss. “But that is now how commercial transactions work.
A mathematician used Fable 5 to disprove a major math problem
The Jacobian conjecture has bedeviled math experts for nearly 90 years.
Shaky political "science": breakdown of the peer review process
[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 December 2025, we examined over 40 studies focused on ranked choice voting (RCV). 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 four articles in our series: the first one summarizing the overall results of our report; the second one analyzed 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; a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research; and a fourth article that analyzed seven studies sponsored by New America’s political reform program that deployed flawed methodology based on “simulated” elections instead of real-world elections, which when combined with some basic misunderstandings of RCV, produced odd and misleading results]. In this fifth article in our “Shaky Political Science” series, we examine two flawed studies on the impact of RCV on voter turnout authored by San Francisco State University professor Jason McDaniel. McDaniel’s research in these two papers stands out for its puzzling methodology, inadequate and cherry-picked data, and faulty analyses that not only misunderstands ranked choice voting but reflects a basic misunderstanding of politics in general. The first of the McDaniel studies actually passed through a peer review process and then was subsequently published by the Journal of Urban Affairs, despite its obvious errors. It is perplexing that a jury of peer political scientists did not catch the errors and send it back to McDaniel for a rewrite or outright rejection. This calls into question the credibility of the peer review process itself. The peer review process is supposed to represent the intellectual pinnacle of the profession – a way for academics to certify the quality of each other’s work, and to act as a check against sloppy research. In the case of the McDaniel study, the peer review process failed. Yet his flawed study has been cited dozens of times by other political scientists in their own flawed works — an unvirtuous circle of academic research. Here is the first Jason McDaniel study that we analyzed: “Writing the Rules to Rank the Candidates: Examining the Impact of Instant-Runoff Voting on Racial Group Turnout in San Francisco Mayoral Elections,” by Jason McDaniel, 2016, Journal of Urban Affairs. This study covered San Francisco’s use of ranked choice voting (RCV) in mayoral elections, purporting to show that RCV reduces voter turnout overall, increases voter errors, and has a negative impact on “marginal populations,” i.e. racial minorities, mainly as the result of the alleged complexity of RCV asking voters to rank candidates and having to be familiar with multiple candidates and related dynamics. McDaniel reached these conclusions by examining five San Francisco mayoral elections taking place in odd years (i.e. where there are no federal or state races on the ballot) from 1995 to 2011, comparing three earlier non-RCV races (in 1995, 1999 and 2003) with the later two RCV races (in 2007 and 2011). Only two RCV races were studied, and as McDaniel acknowledged, only the contest in 2011 was “competitive” (that contest in 2007 was the first mayoral election using RCV, and incumbent mayor Gavin Newsom won easily in the first round of counting, earning 74% of first choices and far ahead of the second place candidate who had just 6.3% of first choices). Yet even the allegedly competitive contest in 2011was won by an incumbent with a landslide margin of nearly 20 points. No other elections were studied by McDaniel, even though San Francisco elects 17 other offices besides mayor with RCV, including the 11 seats on the Board of Supervisors (the name for the city council) in even years, and six other citywide offices in odd years. Previous academic research has found that competitive elections can stimulate greater voter involvement and higher turnout. For unexplained reasons, this study ignored at least 34 other RCV races between the years 2004 to 2011, many of which were very competitive and had high voter turnout. Instead, McDaniel’s analysis based its conclusions on data from only a single, marginally-competitive mayor’s race using RCV. These 2007 and 2011 mayoral elections were the only two RCV elections that McDaniel used as the sole basis for his small data set to determine the impact of RCV on voter turnout. Despite having no meaningful data to inform his study, McDaniel nonetheless concluded that RCV lowered turnout when compared to the previous three mayoral non-RCV elections using two-round runoffs. Then, using the same inadequate database of San Francisco elections, McDaniel also found that “Instant-runoff voting is associated with a significant decline in Black voter turnout and White voter turnout…On average, Black voter turnout decreases by 18 points and White voter turnout decreases by 16 points …” But how could he credibly arrive at that conclusion when his study was based on voter turnout in two RCV contests that were not even remotely competitive? Beyond that, McDaniel tacitly admits that the key determining factor driving Black voter turnout was not RCV vs non-RCV elections, but the presence of a viable Black candidate. McDaniel wrote, “Black voter turnout was about 17 percentage points higher for the two elections that featured an African American candidate compared to the three elections that did not” — but one of those three elections did not use RCV. The same dynamic was present for White voter turnout, in which higher turnout was not correlated with RCV vs non-RCV elections, but the presence of a viable White candidate. Beyond that, McDaniel’s voter turnout results, whether correlated with the race of the candidates or RCV vs non-RCV elections, showed no clear patterns. In his Figure 1, he shows low Asian voter turnout in the non-RCV elections in 1999 and 2003 when there was no viable Asian candidate running, but in the 2007 mayoral election using RCV, there is still no viable Asian candidate, yet Asian turnout increases dramatically. For Latinos, voter turnout is virtually the same in 1999 (no viable Latino candidate, non-RCV elections), 2003 (leading Latino candidate, non-RCV elections), 2007 (no viable Latino candidate, RCV elections) and 2011 (leading Latino candidate, RCV elections). Meanwhile White voter turnout increased dramatically from a low in the 2007 mayoral election, in which the leading candidate was a white incumbent, to the 2011 election in which there were no leading white candidates that finished any higher than sixth place with 5.6% of the vote. Clearly other factors were determining voter turnout than either the race of the candidates or RCV vs non-RCV elections. Despite these inconsistent findings, McDaniel boldly marches forth to render conclusions about the impact of RCV on different groups of racial voters, even though his conclusions are not remotely supported by his data. This looks like the worst kind of “data cherry picking” to cover up a puzzling conclusion based on a poor data set involving only two RCV elections, neither of which were remotely competitive. McDaniel’s cherry picking didn’t stop there. In calculating voter turnout for the three non-RCV mayoral elections in 1995, 1999 and 2003, he failed to mention that in each of these years there was a two-round runoff election, with a first round election in November in which no candidate garnered a winning majority of the vote, followed by the decisive runoff in December. So there were two non-RCV elections in each election year. But McDaniel only decided to include one of the two elections in his data set, apparently the December runoff in each year (which had higher turnout in two out of the three mayor’s races, and lower turnout in the third). So he ignored the November 1995 election, the November 1999 election when turnout was only 45 percent, and the November 2003 election when turnout was only 45.7 percent. This is flawed methodology based on blatant cherry picking, since McDaniel simply omitted three elections worth of data that did not fit within his frame. His study should never have made it through a peer review process, and it is an embarrassment to political science that not only was it approved for publication, but it has been uncritically cited by dozens of other political scientists. Share A “study” without wider context or credible data Lacking a data set that included even a single competitive RCV mayoral race, McDaniel declared that RCV lowered voter turnout in 2011, when turnout was 42.5 percent. But he also ignored -- or did not realize -- that voter turnout in mayoral elections during 2011 had sharply declined in all big cities, including far more steeply in nearby Los Angeles and other cities. In fact, San Francisco’s election in 2011 had the second highest turnout of any mayoral election in the nation’s 22 largest cities from 2008-2011. Was 2011 just a particularly low turnout year all across the country, perhaps because the country was still reeling from the aftermath of the housing market collapse in 2008-2010? McDaniel ignored this factor and did not search for alternative explanations beyond his flawed thesis. Furthermore, while McDaniel suggested that RCV leads to more “overvotes” – which is an invalidated ballot in which a voter, in either a non-RCV or RCV race, makes a mistake and selects two candidates for a single choice – he failed to address that in the June 2012 US Senate primary contest, which used the typical “one-choice” plurality method and did not use RCV, there was more than five times as many overvotes in San Francisco and Oakland than in the RCV mayoral elections in those cities. Additionally, turnout had been significantly higher and more representative in RCV elections for the San Francisco Board of Supervisors races -- that McDaniel didn’t even bother to study -- as compared to previous “delayed two-round runoffs” for those offices, often higher by as much as 40% in the RCV races. Perhaps the most surprising irony was that an earlier study co-authored by McDaniel (Overvoting and the Equality of Voice under Instant-Runoff Voting in San Francisco by Francis Neely and Jason McDaniel, 2015, California Journal of Politics and Policy) found that overvotes are often more common in lower-income precincts, but that the pattern of overvoting is similar in both RCV and non-RCV contests. Wrote Neely and McDaniel: “[Overvote] errors appear to be a function of complexity in general and not IRV per se…. the pattern of overvoting is similar in both IRV and non-IRV contests…Even among votes cast in a top-of-the-ticket race like U.S. Senate, overvoting occurs in an uneven fashion” (IRV, or ‘instant runoff voting,’ is another name for ranked choice voting in single-winner contests). So McDaniel not only contradicted his own conclusion in his earlier study with Neely, but the earlier study had been an important corrective for one of the most frequently made mistakes made by anti-RCV political scientists – a continual failure to provide crucial context by pairing a comparative analysis of real world RCV elections with an analysis of real world plurality or other electoral methods. Unfortunately McDaniel’s earlier study does not get cited much by other researchers, and McDaniel did not even cite it in his own flawed study analyzed above. Incredibly, McDaniel cherrypicked McDaniel, even as his flawed study has been cited dozens of times. Despite being a textbook example of poor political “science,” featuring both inadequate as well as cherrypicked data, the shelf life of McDaniel’s study has been extended endlessly by other political scientists and media outlets who have cited it uncritically. Equally troubling, the Journal of Urban Affairs put this paper through its peer review process and published it despite its numerous flaws. McDaniel gets it wrong, Part II Not content with producing one shoddy, confused paper, McDaniel produced another one a few years later, here is the link: Electoral Rules and Voter Turnout in Mayoral Elections: An Analysis of Ranked-Choice Voting by Jason McDaniel. San Francisco State Univ. (McDaniel, 2019). In this paper, McDaniel extended his previous flawed research focused on San Francisco, about voter turnout and voter impact, to multiple RCV cities through the 2018 elections. In his data set he included San Francisco, Minneapolis, St Paul, Oakland, Berkeley, San Leandro and Santa Fe. He then compared their turnout in mayoral elections to non-RCV cities, and to mayoral elections in those cities held before first RCV usage, and concluded “a significant decrease in voter turnout of approximately 3–5 percentage points in RCV cities after the implementation of RCV.” But he replicated many of the same mistakes as his first study presented above. While in this study McDaniel attempted to distinguish between various factors, such as odd year vs. even year elections, competitive races vs. noncompetitive, contests with open seats and other factors, the analysis is superficial and inadequate. He attempts to incorporate so many often-conflicting variables that his conclusions are once again unsupported by the actual data, and ultimately unconvincing (this second study never underwent a peer review process). For example, McDaniel declared that for RCV cities “in elections that are more competitive than average (margin of victory less than 20%), there is no significant RCV effect on voter turnout.” Yet McDaniel is a professor in a city, San Francisco, in which the 2018 special election using RCV to fill a mayoral vacancy was exceedingly close, decided by a margin of only one percentage point, and resulted in the second highest turnout in San Francisco history for a mayoral election. Illustrating the complexity involved in assessing the critical factors in turnout, that special election was held on the same election day as federal and state offices, which tends to boost turnout. Yet more voters participated in that RCV mayoral election than voted on the same day in the non-RCV primary for governor in which former San Francisco mayor Gavin Newsom was running. Even more puzzling, in this study McDaniel inaccurately cited his own previous study from 2015 with Neely. McDaniel wrote, “Neely and McDaniel (2015) examine individual ballot image files within a natural experiment, and find higher rates of ballot errors in RCV elections compared to non-RCV elections” (emphasis added). However, we quoted from that 2015 study several paragraphs above, and in actual fact Neely and McDaniel actually concluded the opposite -- that the pattern of overvoting – which is the most common form of ballot error – is similar in both RCV and non-RCV contests. McDaniel also ignored the fact that in Bay Area RCV elections held in even years – at the same time as federal and state elections, when turnout is highest -- the “undervote” (that is, the dropoff in ballots cast for governor or mayor) has declined, which contradicts his thesis that the act of ranking ballots in RCV elections is too complex and presents a barrier for certain demographics of voters. To his credit, McDaniel actually acknowledged another contradiction in his own paper, which found a negative RCV turnout effect in some odd-year elections but not others, admitting that “may present a challenge to the theory that increased complexity [from RCV] will have a marginal negative effect on voter turnout.” But he tried to wallpaper over that contradiction by blaming it on an unlikely source -- bad election administration. McDaniels wrote, “This would suggest that the negative impact of RCV on voter participation may be alleviated by high quality election administration,” without presenting any data or even anecdotes to support such an unfounded theory. While competent election administration of any election, whether RCV or non-RCV, is crucially important, there is no credible research that supports the far-fetched notion that the quality of election administration has much of an impact on voter turnout in US elections, whether using RCV or otherwise. Upgrade to a $5 subscription The unvirtuous circle of academic research It is puzzling how McDaniel’s first study could have survived the peer review process for the Journal of Urban Affairs. The mistakes in McDaniel’s first study above should have been obvious to anyone with a basic understanding of ranked choice voting as well as elections in general, and elections in San Francisco specifically. It undermines faith in the credibility of the peer review process to think that a jury of peer political scientists did not catch the egregious errors and reject McDaniel’s sloppy research and paper. And it remains a black mark on political science that this thoroughly discredited study has been cited dozens of times by other researchers. Indeed, in our research paper it became apparent that many political scientists cite each other’s flawed research to pad their list of citations and to legitimize their own flawed research, creating an unvirtuous circle of political science. More broadly, McDaniel’s voter turnout studies, like other studies evaluating RCV’s impact on turnout, lacked a recognition that no credible opinion or research can claim that RCV increases voter turnout merely because so many voters are excited to vote for their favorite candidates instead of “lesser evil” candidates without worrying about spoilers. Instead, with respect to turnout, the advantage of RCV has always been that, in most places where it has been used, it has eliminated a pre-November primary or post-November runoff, which usually saw about half the turnout of the November general election. This is especially true during even years when there are state and federal elections on the ballot which substantially drive turnout more than local contests. Any study that does not adequately account for this non-November primary/runoff vs November general election dynamic, or odd year vs even year election factors, is simply not credible. Both of McDaniel’s studies largely failed on those grounds. Steven Hill and Paul Haughey Share Upgrade to a $5 subscription Thanks for reading DemocracySOS…your digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
A Mathematician Used Claude Fable to Disprove the 87-Year-Old Jacobian Conjecture in a Few Hours
Levent Alpöge, a number theorist at Anthropic, announced on X that he had found a counterexample to one of algebraic geometry's most notorious problems, crediting Fable as his collaborator.
Op-Ed: School employees' health costs are everyone's problem
As premiums soar by more than a third, lawmakers have an opportunity to change how we bargain for coverage
Scientists used children’s ‘silly sprinklers’ to solve a physics problem Richard Feynman could not; the reverse version spins 50 times slower
If you’ve ever watched a “silly sprinkler” in action (those bright, looping contraptions that twist and spray water in wild patterns) you’ve probably just seen them as summer fun. But a group of mathematicians have turned these backyard toys into tools for solving a famous physics puzzle that stumped even Nobel Prize–winning physicist Richard Feynman.
Cincinnati businesses, restaurants clean up in wake of flood aftermath
Afternoon storms on July 17 brought heavy rain to Greater Cincinnati, leading to significant flooding that forced the closure of local businesses.
Lab coats before graduation: Texas Academy of Mathematics and Science students take on research at UNT
Rows of bubbling fish tanks line the Roberts Lab at the University of North Texas as some of the students at the Texas Academy of Mathematics and Science spend their
Alabama students show significant gains in science proficiency; reading, math results mixed
By Andrea Tinker, Alabama Reflector Alabama students made significant improvements in science proficiency on this year’s Alabama Comprehensive Assessment Program (ACAP) standardized test, while the picture was somewhat more mixed on reading and math scores. According to data released by the Alabama State Department of Education on Tuesday, science proficiency went up for fourth, sixth […]
Secret Lair: A Marvelous Mathom Superdrop
Pack your pocket-handkerchief, polish your buttons, and mind the Dragon—The Hobbit comes to Secret Lair on August 17, 2026!
Reading Science Center receives $10,000 grant for Sisters in STEM program
The Reading Science Center has received a $10,000 grant from the American Association of University Women (AAUW) to support its Sisters in STEM program, an initiative designed to encourage girls to explore science, technology, engineering and mathematics. The grant was awarded by AAUW through the organization’s Reading Branch. Sisters in STEM is a weekly program […]
Maine School of Science and Mathematics adds outreach leader
The Maine School of Science and Mathematics is a statewide STEM-focused high school that offers an advanced college-preparatory curriculum.
The World Cup used to let us micro-dose politics. Now it is engulfed by it
Whether it is our ‘identity maths’, naked nostalgia or diaspora experience. All seem scarred by the political contexts bearing down on this tournament
Alabama students make big gains in science, improve across eighth-grade tests
Since 2023, eighth-grade proficiency has climbed from 50% to 59% in English language arts and from 21% to 27% in math.
Preliminary ACAP test scores show improving science, stagnant ELA and math scores
The Alabama State Board of Education is sharing preliminary data on spring 2026 test scores for the Alabama Comprehensive Assessment Program (ACAP).
Preliminary ACAP test scores show improving science, stagnant ELA and math scores
The Alabama State Board of Education is sharing preliminary data on spring 2026 test scores for the Alabama Comprehensive Assessment Program (ACAP).
Fort Pierre couple’s love of math, science, engineering still inspiring South Dakota youth
For Gary and Connie Grittner, investing in young people has always been about creating opportunities that can change the course of a life. Gary knows this firsthand. As a young teen, he attended a summer science camp that sparked his own interest in STEM (science, technology, engineering and mathematics). That experience led him to pursue his doctorate in chemistry and reinforced his belief that exposing students to STEM at an early age can shape their futures. [ ](https://www.thedakotascout.com/p/alleged-cryptocurrency-conman-released) [ Alleged cryptocurrency conman released pending fraud trial ](https://www.thedakotascout.com/p/alleged-cryptocurrency-conman-released) Jonathan Ellis · 11:03 AM [ Read full story ](https://www.thedakotascout.com/p/alleged-cryptocurrency-conman-released)
A Science Museum program is giving students a spot on the Minnesota Vikings
Through its Vikings Voyage program, the Science Museum of Minnesota is providing students with a look inside the science, technology, engineering, and math (STEM) careers behind their favorite football team.
Chamath Palihapitiya: Some CFOs are in for a tokenmaxxing shock
AI spending ballooned as companies pushed workers to use more tokens. Chamath Palihapitiya says CFOs may soon feel the hangover.
We Finally Know The Name of a Revered Maya Astronomer-Mathematician: Sak Tahn Waax
The 'white-chested fox'.
FULL SHOW: Inside INdiana Business 7/12/26
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How to Embrace the 'Science of Math' Without Abandoning Your Existing Curriculum (Opinion)
Teachers can improve mathematics instruction with these straightforward changes.
Metro Detroit businesses pick up the pieces after days-long power outage
Small business owners in Metro Detroit are frustrated in the aftermath of the power outage that impacted services for several days.
The ‘leading ladies’ of Science — inside the Microbiology Girls Club
It’s part science club, part mentorship program and part launchpad for the next generation of women in Science, Technology, Engineering, and Mathematics (STEM). But what really connects the girls inside this microbiology lab is curiosity and a community built on asking (and answering) questions.
Engineers solved an airflow mystery hidden nearly a mile underground
Engineers at a deep underground research facility noticed something strange during major rainstorms: airflow underground sometimes reversed direction. Using new sensors and mathematical modeling, they found that water rushing down a shaft was effectively pushing air through the tunnels like a giant piston. The breakthrough explains a long-standing mystery and could help underground operations better predict and manage ventilation during storms and emergencies.
DC experiences ‘Code Red’ air quality after massive fireworks display
Residents of Washington, DC, on Sunday briefly experienced the worst air quality of any major city in the world, according to a company that ranks pollution globally, as the capital reckoned with the aftermath of a massive, 40-minute Fourth of July fireworks show.
Latest penalty miss brings up bad omens for Brazil 👻
South American giants Brazil had a massive opportunity to go up 1-0 over opponents Norway during Sunday's Round of 16 clash. But they went on to squander it. Manchester United star Matheus Cunha was b...
Mavs Should Make Bennedict Mathurin a Priority in Free Agency
The Mavericks want to build around Cooper Flagg for years to come and Bennedict Mathurin’s age and scoring ability align with that timeline.
FULL SHOW: Inside INdiana Business 7/5/26
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Audit spots some errors, but gives Wyoming Business Council passing grade
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For the 2014 film Interstellar, director Christopher Nolan hired Caltech physicist Kip Thorne as his science consultant — and the visual simulations Thorne helped design for the film's black hole were so mathematically accurate that they generated two peer-reviewed scientific papers and became the model for how astrophysicists visualize black holes today
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Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
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FULL SHOW: Inside INdiana Business 6/21/26
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A team led by a geophysicist in Italy now believes that about 90% of Earth’s fresh water ice may not be on solid ground after all.
Business Restructuring Review Vol. 25 No. 3 May-June 2026
In This Issue Texas Bankruptcy Court Adopts Barnet Rule Requiring Foreign Debtor to Have U.S. Assets to Be Eligible for Chapter 15 Relief In In re Siu-Fung Ceramics Holdings Limited, No. 24-33299, 2026 WL 382424 (Bankr. S.D. Tex. Feb. 10, 2026), the court denied petitions for chapter 15 recognition of Hong Kong bankruptcy and liquidation proceedings commenced on behalf of an individual debtor and a group of companies that he owned or controlled. According to the court: (i) the corporate debtors' Hong Kong liquidation proceeding was not eligible for chapter 15 recognition because the attorney retainer upon which such relief was based was not funded until nine months after the chapter 15 petition date; and (ii) the individual debtor's Hong Kong bankruptcy case could not be recognized because the debtor's "center of main interests" on the chapter 15 petition date was not in Hong Kong, and he did not even maintain an "establishment" there. [read more …] Southern District of Texas Clarifies Post-Purdue Course for Consensual Third-Party Releases and Gatekeeping Provisions In In re Container Store Group, Inc., 676 B.R. 356 (S.D. Tex. 2026), appeal filed, No. 26-20166 (5th Cir. Apr. 14, 2026), cross appeal filed, No. 26-20166 (5th Cir. Apr. 24, 2026), the U.S. District Court for the Southern District of Texas addressed consensual third-party releases and gatekeeping provisions in the aftermath of the U.S. Supreme Court's ruling in Harrington v. Purdue Pharma L.P., 603 U.S. 204 (2024). Consistent with existing Fifth Circuit precedent, the court confirmed that, in appropriate circumstances, consent for third-party releases may be obtained through a creditor's failure to opt out of granting that consent. But, the court ruled, consent through an opt-out mechanism essentially can never be inferred for creditor classes that stand to receive no recovery under a chapter 11 plan and that are deemed to reject the plan by operation of applicable bankruptcy law. Instead, those classes must affirmatively opt in to any release. The court also narrowed and remanded the bankruptcy court's acceptance of a broad gatekeeping provision, ruling that such provisions must correspond to the restrictions established by Fifth Circuit precedent. [read more …] Delaware Bankruptcy Court: Initial Transferee Need Not Be Defendant in Fraudulent Transfer Avoidance and Recovery Litigation The Bankruptcy Code gives a bankruptcy trustee the power to avoid asset transfers that are either made with the intent to defraud creditors or that are "constructively" fraudulent because an insolvent debtor did not receive adequate value in exchange. The Bankruptcy Code also permits recovery of an avoided transfer from not only the initial recipient, but also from subsequent transferees. Courts disagree over whether a transfer must actually be avoided before the trustee can seek recovery from a subsequent transferee and whether the initial transferee must also be a defendant in the recovery litigation against a subsequent transferee. The U.S. Bankruptcy Court for the District of Delaware weighed in on this debate in Phillips v. SS Associates LLC (In re ONH AFC CS Investors, LLC), No. 23-10931 (CTG), 2026 WL 312892 (Bankr. D. Del. Feb. 4, 2026). The court adopted the majority view, ruling that the initial transferee need not be a defendant in avoidance and recovery litigation against a subsequent transferee, but that the trustee must establish the elements necessary to avoid the initial transfer as part of the recovery litigation. [read more …] New York Bankruptcy Court Weighs In on Adequate Assurance of Future Performance under Assigned Leases and Chapter 11 Plan Exculpation Provisions In order to assume and assign an executory contract or unexpired lease, a bankruptcy trustee must cure most defaults under the agreement and provide "adequate assurance" of the assignee's future performance. However, the Bankruptcy Code provides no guidance as to the meaning of "adequate assurance" other than with respect to shopping center leases. In In re Broadway Realty I Co. LLC, No. 25-11050, 2026 WL 147505 (Bankr. S.D.N.Y. Jan. 19, 2026), the U.S. Bankruptcy Court for the Southern District of New York examined this issue in connection with a proposed assignment of thousands of residential leases as part of a free-and-clear sale of debtors' properties. The court held that "adequate assurance" does not mean a guarantee of the assignee's future performance, but merely demonstration that the assignee has the ability to satisfy the underlying obligations. The bankruptcy court also ruled that an exculpation provision in the debtors' chapter 11 plan exonerating non-estate fiduciaries from liability for actions taken in connection with the chapter 11 case was unobjectionable, provided it was limited in scope to postpetition conduct and actions taken in connection with implementation of the plan after its effective date. [read more …] Newsworthy Corinne Ball (New York) (Hall of Fame), Bruce Bennett (Los Angeles), Carl E. Black (Cleveland), Jeffrey B. Ellman (Atlanta), Brad B. Erens (Chicago), Gregory M. Gordon (Dallas), Heather Lennox (Cleveland/New York), Joshua M. Mester (Los Angeles), and Kevyn D. Orr (Washington) were included in the 2026 edition of Lawdragon 500 Leading Bankruptcy and Restructuring Lawyers. The Daily Business Review selected Isel M. Perez (Miami) as an "On the Rise" winner for the Florida Legal Awards. The Florida Legal Awards annually recognize those who have made significant contributions to the legal industry in Florida. Dan T. Moss (Washington/New York) was inducted as a Fellow in the 37th Class of the American College of Bankruptcy at its Annual Meeting in March 2026 in San Antonio, Texas. Christine Borries (Munich) was ranked in the practice area Restructuring/Insolvency in the 2026 edition of Chambers Germany. The 2027 edition of The Best Lawyers in Singapore™ recognized Sushma Jobanputra (Singapore) in the practice area Insolvency & Reorganization. The restructuring transaction involving Eissmann Automotive North America, Inc. received the "2025 Turnaround of the Year (Large Company)" at the Turnaround Management Association (Detroit) Annual Awards Reception. T. Daniel Reynolds (Cleveland) and Nick Buchta (Cleveland) represented subsidiaries of Eissmann Group Automotive in connection with the parent company's German insolvency proceedings. Roger Dobson (Sydney) is among 40 Jones Day lawyers recognized by The Best Lawyers in Australia 2027 for his contributions in Banking and Finance Law; Distressed Investing & Debt Trading; Insolvency and Reorganization Law; Litigation. An article written by Corinne Ball (New York) titled "Ard Finance—A Global Enterprise Combining an LME With a Foreign Insolvency Proceeding and Chapter 15" was published in the April 21, 2026, edition of the New York Law Journal. An article written by Alex Sapp (New York) titled "Recent Developments in Section 510—Subordination" was published in the 2025 edition of the Norton Annual Survey on Bankruptcy Law. An article written by Brad B. Erens (Chicago) titled "New York Bankruptcy Court Approves Insider DIP Litigation Financing" was published on April 6, 2026, by Lexis Practical Guidance. An article written by Corinne Ball (New York), Dan T. Moss (Washington/New York), David S. Torborg (Washington), and Isel M. Perez (Miami) titled "New York District Court Affirms Narrow Scope of 'Public Policy Exception' in Chapter 15 Cases" was published on April 6, 2026, by Lexis Practical Guidance. An article written by Jeffrey B. Ellman (Atlanta) titled "Statutory Cap on Lease Rejection Damage Claims in Bankruptcy Applies to Solvent Debtors" was published on April 6, 2026, by Lexis Practical Guidance. An article written by Trisha L. Mowbray (Real Estate; Chicago) and Ryan Sims (Washington) titled "Chapter 11 Plan that Abridged Non-Debtor Lessee's Rights to Remain in Possession After Rejection Unconfirmable as Having Been Proposed in Bad Faith" was published on April 6, 2026, by Lexis Practical Guidance. Lawyer Spotlight: Ben Rosenblum Ben Rosenblum, a partner in the New York Office, represents debtors, creditors, and other parties in out-of-court restructurings and court-supervised proceedings, including chapter 11 cases, municipal bankruptcies, international insolvency matters, and stockbroker liquidations. His experience spans industries such as manufacturing, shipping, mining, aviation, energy, real estate, and telecommunications. Ben's notable debtor representations include Peabody Energy, the City of Detroit, Chrysler LLC, Spansion Japan, and Dana Corporation. Significant creditor representations include General Maritime Corporation, Calpine Corporation, American Home Mortgage, and Murray Energy. He currently represents creditors in Puerto Rico's restructuring. He also has extensive experience advising financial institutions on bankruptcy law as applied to derivatives.
Why the GOP’s voter fraud theories in California don’t make sense
The last week has brought the largest resurgence of election denialism since the aftermath of the 2020 election. Many on the right have cried foul over late shifts toward Democrats in results from California’s June 2 primary.
Student-led summit connects local youth with global peers through science, technology, engineering, math
Hawaiʻi International Science Experience is hosting a five-day program designed to break barriers and connect local and global youth in STEM.
Florida House argues Fair Districts amendment should be tossed in full
If justices toss the language in the aftermath of a Callais decision, that could open the door to legislative redistricting.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
NJ ranks high in education, healthcare in annual report on child well-being
A new report says New Jersey ranks near the top nationwide for education and health, but most kids struggle with reading and math.