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Weiser joins lawsuit challenging new fuel economy standards
“The federal government’s backsliding on standards for the next five years requires less fuel economy than what the U.S. fleet of new cars and trucks achieved in 2021. Rolling back fuel efficiency standards will cost Coloradans even more at the gas pump, harm public health, and undermine our nation’s position in the EV market. These clean car rules are part of our clean energy future and that’s why we are challenging this illegal action,” said Attorney General Weiser.
Studying Science in an Anti-Scientific Time
This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe
Wynonna Judd calls out Rosie O’Donnell after political comments leave country star ‘in shock’
Wynonna Judd pushed back after longtime friend Rosie O'Donnell labeled her a Trump supporter, calling the claim a "complete assumption."
National Science Foundation Diverted $1.4 Billion in Science Funds Mandated by Congress
The move, affecting about one-fifth of the National Science Foundation’s primary research budget, is the latest sign of how the White House is asserting more control over grants.
DA proposes civil commitment court during mental health symposium
Salt Lake County DA Sim Gill is proposing a civil commitment court as an alternative to jail for those suffering from a severe mental illness.
Bishop’s computer science chair says AI is eliminating ‘grunt work’ and reshaping digital jobs
Dr. Russell Butler, chair of Bishop’s University’s Department of Computer Science, says artificial intelligence is increasingly taking over routine digital “grunt work,” changing the skills students and workers may need in the years ahead. Photo courtesy of Bishop’s University. Click here to read our platform! Get local news like this every morning. Subscribe free below. Subscribe Pulse Newsroom Artificial intelligence is already taking over some of the routine digital work once performed by entry-level employees, according to Bishop’s University Computer Science chair Dr. Russell Butler — a shift he believes will make expertise, judgement and the ability to evaluate AI-generated work increasingly important. Butler described the change as automation reaching what he calls the “bottom of the cognitive pyramid” during an interview with The Pulse about AI, university education and the future of digital work. His argument is not that humans are disappearing from the workplace. Instead, he sees their role changing as AI becomes capable of handling more of the repetitive tasks involved in computer-based work. “Everything’s converging on just AI basically doing all the grunt work,” Butler said. The bottom of the cognitive pyramid Butler is from Lennoxville, completed his undergraduate studies at Bishop’s and later earned his PhD at the Université de Sherbrooke. He joined Bishop’s in 2019 and now chairs its Department of Computer Science. He uses AI in research, teaching and other creative work, and said the technology is increasingly capable of handling routine digital tasks that once required substantial human time. “You’ve got the grunt work, like moving things around in Excel, doing basic programming tasks,” Butler said. “That’s like the bottom grunt work that the entry-level worker would have done two or three years ago.” Butler went further, saying in his view that much of this type of work has already been absorbed by AI tools. He connected that change to a shift he sees taking place in computer science, where entry-level roles have declined while experienced workers can use AI to increase what they can produce. “A senior developer now has access to essentially an unlimited amount of junior developers on demand,” Butler said. He was describing AI systems metaphorically, not literal employees. His point was that one experienced developer can now ask AI tools to produce code or other technical work that might previously have been assigned to more junior workers. That raises a broader question for universities and employers. If some of the simpler work traditionally given to newcomers can now be done by machines, how do people gain the experience needed to become senior workers themselves? Students doing work that once required years of programming Butler said he is already seeing the change inside his classroom. He created a Bishop’s course called CS208, Coding in Plain English, to show students — including those without traditional programming backgrounds — what they can accomplish using AI. Some come from disciplines such as psychology or neuroscience with no previous programming experience. “What I’m finding is that students who have zero programming experience, they come in from another discipline like psychology or neuroscience or something, they are now able to get up and running with real data sets,” Butler said. Butler’s own doctoral work involved neuroimaging, and he said he has assigned students work he considers comparable to the kinds of analysis he performed during his PhD. He described giving students a real dataset, an expected result and instructions to use AI to complete the analysis. Butler said those students have been able to carry out work at a level he associates with tasks he once performed after years of programming experience. That does not mean a student using AI instantly acquires the same knowledge as an experienced computer scientist. But Butler sees it as evidence that AI can dramatically lower the technical barrier between having a problem to solve and producing a working result. The role of the person using the system then changes. They need to know what to ask for, understand enough about the subject to judge the answer and take responsibility for what is ultimately produced. What about AI hallucinations? One persistent concern surrounding generative AI is hallucination — when a system produces information that sounds convincing but is false or unsupported. Butler said that problem has improved substantially in newer systems and is not as severe as it was with earlier models. He pointed in particular to newer AI systems’ ability to search the web, consult scientific databases and return source links rather than simply generating a citation from their internal model. Butler nevertheless said users should verify what AI produces. “You should verify,” he said while discussing citations generated with AI. His position is that hallucinations have become less common in the systems he uses, not that incorrect AI output is impossible. Human checking remains part of the process. Hours of work compressed Butler offered another example from his administrative work at Bishop’s. The university has been comparing CEGEP courses with first-year Bishop’s courses to determine where students may qualify for advanced standing. Traditionally, Butler said, that means reviewing two sets of course descriptions and manually matching one institution’s courses with another’s. As department chair, that could require hours of reading and comparison. He said AI can now be given both sets of material and asked to generate an initial mapping automatically. “It saves me hours and hours of work,” Butler said. Butler said the AI-generated mapping can also produce a better result than he would produce himself. He sees similar opportunities in businesses. A company may receive specifications, drawings, emails or other digital material from customers and then have employees convert that information into an internal format before anything can be manufactured or delivered. AI can increasingly perform parts of that intermediate processing. In Butler’s view, the employee does not necessarily disappear. Instead, the job becomes more about checking the result. “The human is still there,” he said. Judgement could become more valuable That change is central to Butler’s view of where digital work may be headed. If AI can produce a first draft, write basic code, compare documents or process large amounts of information, he argues that knowledge and judgement become increasingly important. “The real scarce resource going forward is going to be domain knowledge, expertise, and… credentials, authority, the ability to work with other people,” Butler said. He is therefore encouraging students not only to learn AI tools but also to broaden their education. He mentioned acting, fine art and photography as examples of courses that can help develop what he described as taste and intuition — the ability to recognize what is good, what is not and what should be changed. “And that’s the skill that they need now because the grunt work has been automated and now it’s about evaluating what the AI produces and telling it how to improve or expand the output,” Butler said. That does not remove the need for technical knowledge. Butler’s argument is that the emphasis may shift away from manually completing every step of a task and toward directing, checking and improving work produced with AI assistance. He also stressed that humans remain responsible for final decisions. “At the end of the day, the human has to make the final call,” Butler said, pointing to the question of accountability. Physical work may be different Butler draws a distinction between digital work and jobs requiring people to manipulate the physical world. He expects AI to continue advancing quickly in tasks performed almost entirely on computers. He is more cautious about claims that humanoid robots will soon be able to replace workers such as plumbers or electricians. Butler said those trades would not be replaced “anytime soon” in his view and described some current robotics demonstrations as overhyped. He described the situation as counterintuitive. “It’s almost like we solved the highest level first,” he said, contrasting advances in digital AI with the harder problem of building robots capable of operating reliably in the physical world. Butler’s argument is that some digital occupations could face automation sooner than trades requiring complex physical work. The wider AI shift in the Townships Butler’s comments come as local institutions are increasingly grappling with how AI should be used. The federal government recently announced up to $162 million over five years for 10,000 AI-related placements through Mitacs. The Université de Sherbrooke told The Pulse that the number of its collaborative research and innovation projects supported through Mitacs has approximately doubled since 2018-19, while the number of placements has approximately tripled. UdeS also said it already has projects involving both AI adoption and the application of AI to other fields. At Bishop’s, Butler said part of his role involves building the Computer Science department’s local presence, connecting students with regional partners and strengthening relationships with the Sherbrooke technology ecosystem. Those developments put universities in a difficult position. They have to prepare students to use the tools employers increasingly have available while also making sure graduates understand enough to recognize when those tools are wrong, incomplete or being applied to the wrong problem. The risk of losing human skills Butler does not see AI as risk-free. Later in the interview, he raised a separate concern: as AI becomes better at reading, writing, programming and other cognitive tasks, people could lose some of the underlying skills that historically helped produce new ideas. “The other danger is that we’re going to lose the human skills,” Butler said. He specifically mentioned reading and writing and argued that people could lose some of the lower-level understanding that contributes to breakthroughs. Butler said AI systems are based on existing human knowledge and argued that they become much more powerful when paired with a capable person. That tension runs through his view of the technology. AI can remove hours of routine work. It can allow beginners to perform tasks that once required substantial technical training. It can give experienced workers significant leverage. But the more capable the technology becomes, the more important it may be for people to develop the judgement needed to decide what should be done in the first place. For Butler, that is where education may have to change. Students still need to understand their field. They still need to know when something is wrong. And they still need to be able to make the final decision. But if AI increasingly handles the bottom of the cognitive pyramid, the value of a worker may depend less on how quickly they can perform the grunt work and more on whether they understand what the work is supposed to accomplish. Don’t miss the next story that affects your community. Join thousands of readers getting The Pulse every morning—plus breaking news alerts when it matters most. If you value this kind of local journalism, consider supporting it. Subscribe, share and like! Subscribe Share
Yankees Postseason Game-Plan Should Reflect Wild Card Series Performance
The New York Yankees have punched their ticket to the 2026 American League Division Series. A victory over the Boston Red Sox in sweep style held various positives. The Yankees need to stick to this game plan to succeed this postseason. Aaron Boone and his team entered this Wild Card Series knowing the term “Bronx Bombers” …
Mars symposium sheds light on life on the red planet
Even the greats like musician David Bowie have asked: Is there life on Mars? In a symposium held earlier this month at William & Mary, Research Professor Joel Levine said we may have answered that question 50 years ago. “The Exploration of Mars & the Search for Life: Viking to Humans” symposium was held in […]
Samsung Galaxy Tab S12 Ultra vs Apple iPad Pro: Here's how they stack up
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Neto: ‘Juventus changed a lot, positive Spalletti impact’
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Idaho ranks second nationwide for small business growth
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Weekly Rundown: Duke University launches digital twin program; Welldoc unveils AI-powered care loop
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Trump to Name Jay Clayton to Serve as A.I. Czar
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Flurry of Lawsuits Shows Both Parties Preparing for Post-Election Challenges
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Voter guide: Prop 134 asks Colorado voters to bar transgender student athletes from certain school sports
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Scientists Untangle the Biology of an ‘Undruggable’ Cancer Gene
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Veteran Business Project Hosts Third Annual Valor to Venture Gala in Naperville Oct. 29
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It’s Time We Reframe Healthcare Organizational Culture Using Behavioral Science
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What’s the best sleep position? It depends.
Also, should you really be using an electronic sleep tracker?
Trinity Health Foundation to host free nursing education event
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11,000+ Washington residents lose healthcare, food benefits
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Would-be Pelosi successors debate who’s more progressive in San Francisco congressional race
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The Latest Challenge to Data Centers? Restive Investors.
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‘Containerabad’: Pakistan’s capital and truckers bleed money amid lockdown
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Proposition 134
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Labor ready to block Johnson’s data center moratorium
The mayor’s proposed yearlong pause is drawing opposition from the same powerful coalition that helped stall earlier pieces of his environmental agenda.
Labor ready to block Johnson’s data center moratorium
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AI blurs lines in campaign ads: ‘People are seeing things that didn’t happen’
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Cambodia: Imprisonment of Activists, Politicians, and Journalists Amid Unrelenting Crackdown
Human Rights Watch thanks the Special Rapporteur for his report, which describes the deteriorating situation for civil society and the political opposition amid an unrelenting crackdown on freedom of expression.
Lubbock business news includes Optimum grant, openings, closings
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A CEO-led group has agreed to buy TNL Mediagene’s Japan business.
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Are Democrats Worried About the Wrong Rich People?
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AIPAC Is Spending Millions to Elect Friends. It’s Also Making Enemies.
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Yankees news: Anatomy of an offensive explosion
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Douglas County should increase number of commissioners
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Time lost to political haggling means lost momentum for research
While it’s not up to universities to set deadlines for EU institutions, “lost time” in the run-up to the launch of the next Horizon Europe would translate into “lost momentum” for universities, according to Dag Rune Olsen, president of the Young European Research Universities Network.“I think it's important to have [the EU budget] in place in order to be able to work on the research funding mechanisms, because we cannot afford to lose more momentum than we already have,” Olsen told Science|Business in an interview in September, just after EU research lobbies gathered in Brussels to push for a €200 billion budget for the next Horizon Europe programme.A funding gap would mean ramping down research activities, said Olsen. “You can do that quite swiftly, that’s not a problem, but ramping up again is much more difficult.”The last-minute push for an ambitious budget, and for a timely adoption of the research programme, comes at a time when the richest EU states have teamed up to call for significant cuts to European Commission’s proposal for the next multiannual budget, which allocates €175 billion for Horizon Europe. At the same gathering of research associations in September, the European Parliament’s budget rapporteur warned that the EU will need to raise its own taxes, otherwise political pressure and tighter national budgets will translate into a limited EU budget with a less ambitious envelope for research. The research sector hopes EU institutions will reach timely agreement on the budget and on Horizon Europe, but progress has been slow. Legislative progress has been delayed, partly because the European Parliament’s budget committee is still haggling over how to apply new EU-wide spending rules, known as the Performance Framework Regulation, and partly because EU governments have yet to agree on the size of the next EU budget. EU research Commissioner Ekaterina Zaharieva told Science|Business in July that she was hoping for an agreement on Horizon Europe by the end of 2026, but the timelines have been pushed back significantly, with the Parliament now aiming to announce its official position on Horizon Europe only in November. Avoid the funding gapAccording to Olsen, delayed EU funds could prompt universities and researchers to move on and do something else, potentially jeopardising years of work building consortia and collaborations. “If funding is interrupted, the networks fall apart in just a few months,” he said. Olsen also noted that an interruption would affect junior researchers first. PhD students and postdocs are on shorter term contracts and universities risk losing them if funding for EU projects is interrupted. Related articlesEU own resources ‘essential’ for future research budget, says leading MEPParliament haggle over EU-wide spending rules delays vote on Horizon EuropeZaharieva hopes for a Horizon Europe deal in 2026Universities usually have more employees, permanent and short-term staff, than they can cover with national funding, and external funding is essential to keep them all on the payroll. In case of an EU funding gap, “there will be a huge number of people that might end up without any contracts,” he said. Apart from the money issue, Olsen said EU projects forces universities to collaborate more which, in turn, increases the quality of teaching and research. In case of a funding dip, people will look around for opportunities elsewhere, in the private sector or abroad. “And then then you have to recruit again, and it's a tedious business,” said Olsen. “A gap is not a good idea, it's actually a waste of money.”According to Olsen, the difficult political context should encourage universities to make a stronger case for the EU increasing research funding, as universities can deliver on competitiveness, technological sovereignty, security and defence. “The reason why we ask for substantial investments in research is exactly what the Commission, what Europe, the Parliament have been discussing,” he said. “You really need to invest in order to thrive in the longer run.”Focus on the member statesBut EU and national R&D expenditure hasn't kept up with the times. EU governments have failed to reach a spending target set almost 25 years ago. Only six EU countries spend 3% or more of their GDP on R&D. Asked whether universities should put more effort into convincing the governments to ramp up their own spending, but also to support a strong budget for Horizon Europe in the ongoing budget negotiations, Olsen said “the answer is yes.”“We are very actively encouraging our members to talk to their ministries in order to enlighten them about the joint effort,” he said. The network is made up of 25 universities from across Europe, all established in the past 60 years or so.Who will call the shots?Budget and timing issues aside, the Parliament’s research committee has been locked in behind-the-scenes negotiations over the future structure of Horizon Europe, with MEPs disagreeing on the degree to which its collaborative research should be governed by councils of experts.The European Parliament’s lead rapporteur on Horizon Europe wants councils of independent experts to decide on the funding agenda, but the Commission, the Council, and parts of the Parliament disagree. For Olsen, it is normal for politicians to point at the big challenges, but when they start detailing how those challenges should be solved, the endeavour “fails utterly.” He does not necessarily think that “bureaucrats who don’t know the field” would be “much better at it” either. “There has to be a combination” of broad political directions and bottom-up implementation, Olsen said.
Good science requires much more than evidence
Empiricists believe that scientific theories are true only if they make correct empirical predictions: if we can't verify these predictions, then there is no reason to expect that they tell us anything about reality. However, Professor of Philosophy at King's College David Kyle Johnson argues that this is mistaken. In response to Bas van Fraassen, who has written on this subject for the IAI, he argues that we have many reasons for believing a theory to be true, such as it being parsimonious and having large scope — qualities which have been used throughout the history of science as criteria for judging theories. In 2024, Princeton philosopher Bas van Fraassen wrote a short article for IAI News entitled “Science Does not Describe Reality: The Limits and Benefits of Explanation.” At the time, I was working on two projects (a science book and a Great Course) that, when completed, would (among other things) defend science’s ability to describe reality. I wanted to write a response to van Fraassen, but the projects demanded too much of my attention. But now that those projects are complete, I am in a position to explain the objections to van Fraassen’s thesis. Summarizing van Fraassen’s argumentIn a nutshell, his argument is this: What scientists do is make predictions based on theories and then invite others to test to see whether those predictions are borne out. “If theory T is right, result R should happen in experimental condition C.” Many think that running an experiment that shows that R happens in condition C is proof that theory T is true (and should be believed). But, van Fraassen argues, that R happened in condition C is only a reason to think that R happens in condition C. That’s the only observable, or empirical, result we can actually see, so that is all that we are really justified in believing. (The underlying theory that made that prediction still may or may not be true.) That is why he calls those who accept his position “empiricists.”Before I explain what is wrong with this position, let me explain what it gets right. What van Fraassen Gets RightFirst, a theory correctly predicting the results of an experiment is not, strictly speaking, “proof” that the theory is correct. The basic line of reasoning is this:If the theory T is true, result R should happen. Result R did happen. Thus, theory T is true.In other words:“If T then R. R. Thus, T.”This is a deductively invalid, fallacious mode of reasoning, called “affirming the consequent.” Those premises do not guarantee that the conclusion follows. “If I am in Pennsylvania, then I am in the USA. I am in the USA. Thus, I am in Pennsylvania.” Clearly that argument doesn’t guarantee its conclusion; neither does the above argument.However, science is not a deductive method of reasoning; it is an inductive one. And inductive arguments merely try to raise the probability of their conclusion, not “prove” them for certain. Thus, the fact that an experimental result doesn’t prove a theory is true is irrelevant; it’s not supposed to. That is not what scientific reasoning is about.___If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational.___Second, it’s also true that a single experimental result doesn’t provide enough justification to believe that a theory is true. Scientists do not come to a consensus about whether a theory is true by looking at a single experiment; they look at all the evidence to determine where the preponderance of evidence points. The mere fact that I am in the USA raises the probability that I am in Pennsylvania, but not nearly enough to conclude that I am in Pennsylvania. However, if I am also regularly encountering people wearing Eagles and Phillies hats and seeing advertisements for Founding Father tours, I probably am. In the same way, a theory successfully predicting the outcome of one experiment is not enough to conclude that it is true; but if a theory makes a wide variety of predictions that are all vindicated by multiple experiments, performed by many different groups and people, all actively trying to prove the theory false (which is what good scientific experiments are designed to do)—that is a good reason to conclude that the theory is true. In other words, if you repeatedly try to prove something false but can’t, that is a very good indication that it isn’t. The problem of underdeterminationIn reply, those whom van Fraassen calls “empiricists” often point to the “problem of underdetermination.” For any given theory that makes successful predictions about what happens in experimental conditions, there are countless other possible theories that make exactly the same predictions—and since they do, the experimental evidence in question is just as much reason to accept any of those theories as it is to accept our original one. Thus, the argument goes, we have no way of knowing whether our original theory is true, or one of the other “predictively equivalent” theories is true. SUGGESTED VIEWING How Occam's Razor changed the world With Johnjoe McFadden As an example of underdetermination, philosophers often offer this example. The evidence of fossils seems to provide reason to conclude that the universe is quite old; ancient fossils are what the “ancient universe” hypothesis predicts. But it’s also possible that God created an ancient-looking universe 200 years ago, complete with a “fake” fossil record for us to find, to make it look old. The existence of fossils is what that theory predicts too. So, the argument goes, the fossil record can’t be used as evidence that the universe is ancient; the fossil record supports the “200-year-old ancient-looking universe” theory just as much. The problem of underdetermination points out that, for any theory with supporting evidence, we could invent another completely different theory that predicts exactly the same evidence, and thus undermine that evidence’s ability to support the original theory.But this “argument from underdetermination” betrays a fundamental misunderstanding of what scientific reasoning is and how it works. To explain, let me use another rather strange (but very real and commonly discussed) philosophical thought experiment called “The Grue Problem.” The “Grue” ProblemSuppose I want to know what color emeralds are, so I observe a few, see that they all appear green, and form the theory that “all emeralds are green.” I thus predict that the next 100 emeralds I find will also appear to be green. And let’s say that happens, and so I thus conclude that my theory that all emeralds are green is true. Now, one might rightly point out that I don’t know for sure that all emeralds are green—I have not seen them all. Maybe some I haven’t found yet are blue. Fair enough. After all, we used to think that all swans were white, until we found some black ones. But let’s say that I go on a quest and collect every emerald in the universe—and, for the sake of the argument, let’s say I can do this, and can even know that I have every one of them. So, I collect them all, look at them all, and verify that they all appear green. Can’t I now rightly conclude that all emeralds are green?Inspired by the problem of underdetermination, a friend might argue that I cannot.Sure, they all appear green now—but there could be another kind of color called “grue.” Something is grue if it is green up to a certain point in time, but then spontaneously turns blue. And there could be an infinite number of “shades” of grue. Something that is one shade of grue will turn blue at noon today, but something that is another shade of grue will turn blue at noon tomorrow, etc. So yes, the emeralds appear green now; and since they haven’t turned blue yet, they are not some “past” shade of grue. But they might be some “future” shade of grue (e.g., they may turn blue at noon tomorrow). And since all future shades of grue are consistent with “being green up to this point in time,” the evidence you have is completely consistent with the notion that all emeralds are some future shade of grue, rather than green. Indeed, that they would appear green until now is what the “some future shade of grue” hypothesis predicts. So, we have just as much reason to conclude that all emeralds are some shade of grue as we do to conclude that they are green.You’re probably thinking “It’s not equally possible that the emeralds are grue; they are obviously green.” You are right! But explaining why your instinctive reaction to my friend’s argument is correct will help inform why van Fraassen’s “empiricist” is wrong and science actually does describe reality.The main mistake my friend makes is in thinking that the “empirical evidence” (of all the emeralds appearing green) is (or even should be) all I am using to draw the conclusion that all emeralds are green. It is not. I am also considering known facts about how colors typically work; things don’t usually change color, and when they do, they don’t do so suddenly, without cause, and all at once. So, even though the simple evidence of the emeralds all appearing green up to this point is consistent with them being some future shade of grue—that is even what the grue hypothesis “predicts”—it is still exceedingly unlikely that they are. The hypothesis that they are green is much more likely. Understanding science refutes the problem of underdetermination The misunderstanding about science that the “empiricist” pointing to “the problem of underdetermination” makes is similar to the one that my friend made. Science is not just about considering experimental evidence; in fact, it’s not even just about considering empirical evidence. As Ernan McMullin and many other philosophers of science have pointed out, the inference that makes science is “inference to the best explanation” (sometimes called “abduction” or “retroduction” by McMullin). While, depending on the circumstances, scientists use many different kinds of reasoning, science as a whole is, simply put, the process of considering multiple competing explanations and comparing them to certain criteria—explanatory criteria (the kinds of things explanations must be and do, by definition, to be a good explanation)—to see which explanation is the best.And these criteria do not deal only with empirical results. Yes, successful predictions raise the probability of a hypothesis, but it’s also the case that the more new assumptions a theory makes, the less likely it is to be true; every new assumption presents an opportunity for the theory to be false. (The fewer assumptions a theory makes, the simpler or “more parsimonious” it is said to be.) Likewise, the more existing evidence a theory contradicts, the less likely it is, because in order for a theory to be true, every piece of evidence it contradicts has to be somehow faulty. (The fewer established theories a new theory contradicts, the more “conservative” it is said to be.) And, obviously, the more something explains, the better explanation it is. (This is called having “scope.”) If a theory raises unanswerable questions, it’s not expanding our understanding, and thus is not wide-scoping. Scientists consider all these things when deciding which theory to accept, because the theory that adheres best to these explanatory criteria is most likely to be true.___the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth.___The reason the grue hypothesis is no good is because it invents an entirely new kind of color—not just a new shade, but an entirely different category of color (a different way colors work)—and thus is not parsimonious. It raises unanswerable questions about how and why emeralds would just spontaneously change from green to blue (and thus does not have wide scope). And it contradicts what is already well established about how color works (and thus is unconservative).The theories that the problem of underdetermination points to do the same thing. Yes, the theory that God created an “ancient-looking universe” (complete with an ancient-looking fossil record) two hundred years ago is also consistent with the empirical evidence (of a fossil record) that presently exists; but that theory is monumentally unparsimonious (it requires many extra assumptions about infinite entities) and not wide-scoping (it raises unanswerable questions about why God would do such a thing). So, I can rationally conclude that it is false.Indeed, the history of science includes many examples of theories that were rejected because they lacked parsimony and/or scope, even before their falsity (and the truth of their competing theory) was confirmed by observation. As I point out in my book, geocentrism was rejected by the scientific community because it lacked parsimony, almost 200 years before the observation of parallax confirmed that the Earth moves around the sun. And the continuum theory of matter was completely rejected by 1860, because it couldn’t explain chemical reactions or organize matter into a periodic table, even though the experiments that eventually confirmed atomic theory weren’t even proposed until the 1870s. Solving the best of a bad lot problemIn response, the “empiricists” are likely to object, this time pointing to the “best of a bad lot” problem. “We have no reason to conclude that an explanation that has been shown to be better than others is true because we have no guarantee that the true theory was among those that we were considering.” If you have a “lot” (a collection) of poor explanations, even the best one is not true.The responses to this problem, however, are numerous. Peter Lipton argues that the set of hypotheses that scientists consider are not just randomly selected and thus unlikely to contain a theory that is true; they use existing knowledge to home in on what is most likely to be true. A doctor trying to explain abdominal pain is not going to bother wondering whether demons are causing it; they are going to stick with things like appendicitis and kidney stones, because they are most likely. Further, despite its name, inference to the best explanation is not just the process of comparing hypotheses and accepting the best one; we also want one that is a good explanation in its own right. If it’s not, we are going to keep looking until we find an explanation that is. And while that process does not guarantee that we will come across the exact truth, it’s very likely that the theory that emerges as the best will be at least approximately true, and thus we will be justified in believing it. And lastly, as I point out in my book, although many theories not based in scientific evidence (like geocentrism and continuum theory) have been completely overturned, science almost always progresses by the refinement of scientifically established theories—not their replacement. (Although our sun is not the center of the universe, heliocentrism was our first step in understanding the Earth’s true place in the universe.) So even if a theory we accept today may not be completely right, accepting it is a step on the journey toward the complete truth. The example of atomic theoryNow, it’s worth pointing out that most empiricists are not skeptical of all scientific theories; they are only skeptical of the ones that hypothesize the existence of “unobservables.” It’s not like they think that round Earth, heliocentrism, and germ theory are just useful (but untrue) “instruments” for making predictions about how to most efficiently navigate the seas, predict planetary motion, and treat diseases. They admit that the Earth actually is round, that the planets revolve around the sun, and that germs exist and cause disease. These facts and entities can be observed; the theories that suggest they exist are true. It’s only when theories start hypothesizing the existence of unobservables—like electrons, or quantum fields—that can’t be observed directly, that we should start doubting the theories’ truth. And to be fair to van Fraassen, in this published work, he doesn’t really say that we should doubt the truth of such theories. Only that scientific reasoning does not rationally force us to accept them as true. Skepticism, or agnosticism, he argues, is warranted. SUGGESTED READING Science is based in metaphor By Andrew Reynolds But the history of atomic theory throws a monkey wrench into that argument. Atoms used to be unobservable; again, scientists first accepted the theory that they exist because it had such grandiose explanatory power. But then atoms were seen with electron microscopes. They thus moved into the category of the observable. But this didn’t substantially change anything about what scientists were justified in believing; it certainly didn’t change any “atomic skeptics’” minds. It just confirmed what everyone already knew! If the hypothesis that something exists clearly provides the best explanation for what we can observe, then the belief that it exists is what is rational. The fact that the thing itself is not technically observable does not make doubting its existence rational.What’s more, the atoms were seen with an electron microscope. But electrons are themselves unobservable. How can an electron microscope reveal an atom if electrons are not real? This leads us to what is famously known as Hilary Putnam’s “miracle argument.” It’s (not) a miraclePhilosopher Hilary Putnam’s miracle argument is somewhat famous. Basically, he argued that a scientific theory being able to repeatedly make successful predictions—even about unobservables—without being at least approximately true would require a “miracle.” In other words, it would be such an improbable occurrence that it could not rationally be believed—the better explanation for why scientific theories about unobservables make the right predictions is because the unobservable entities and structures they hypothesize exist.Van Fraassen has addressed this argument. Indeed, in his original article, where he references Alison Gopnik’s point about orgasms not being “necessary for procreation” even though the desire for orgasm “drives the creation of progeny,” he is pointing to his own argument about how organisms are not naturally selected by evolution to get true beliefs; to the extent that belief-forming processes are selected for, the ones that are selected are the ones that are useful for survival—not the ones that produce true beliefs. In the same way, van Fraassen argues, scientific theories are accepted, not because they are true, but because they aren’t falsified; they make successful predictions; that’s what makes them “stick around.” So, the fact that they make successful predictions (and thus became accepted) is not a reason to think they are true.This argument is similar to C. S. Lewis’s “argument from reason,” which he used to argue against evolution: “The truth of evolution undercuts our ability to know that it is true.” Lewis’ argument famously failed; Elizabeth Anscombe, by Lewis’s own admission, demolished Lewis in a debate on the topic, and I have explained elsewhere why modified versions of the argument fail as well. In short, it’s because it fails to recognize that a belief-forming process can make an organism more likely to survive because it reliably produces true beliefs. It is therefore not the case that beliefs generated by naturally selected belief-forming processes are automatically false or unjustified.In the same way, the reason a theory “sticks around” could be because it’s not falsified, because it “makes correct predictions.” But the reason it makes correct predictions is most likely because it is true. (In pointing this out, I am echoing the arguments of Musgrave, Lipton, Psillos, and Kitcher, who refuted van Fraassen’s argument and analogy long before his 2024 article.) It would be a huge coincidence that it could do so without being true. And that is the point of Putnam’s miracle argument. Thus, contrary to van Fraassen’s suggestion, the mere fact that a theory’s predictive success is why it “sticks around” or “becomes accepted” in the scientific community is not a reason to be skeptical about its truth. ConclusionLet me close by saying something about the fact that empiricists don’t doubt the truth of all scientific theories, just the ones that hypothesize unobservables. In today’s society, this theory is dangerous to espouse publicly. The nuance between being skeptical about all of science, and just the highly technical theories about unobservables, is guaranteed to be lost to the average reader. And there is so much unjustified skepticism and doubt about the findings of science and experts that a headline like “Science Does Not Describe Reality” could easily fuel the anti-science movement, worsen climate change denial and cause the next pandemic.This is why I believe it was important to still respond to van Fraassen’s article, a full two years after he wrote it. Not only, as I have argued, does it represent a philosophically inaccurate view of how scientific reasoning works and what scientific reasoning can do, but it is a dangerous view to boot. It should thus be rejected.
Lamine Yamal says ‘I don’t care’ after stunning England
Musiu0/Depositphotos Lamine Yamal posts message after Spain win Lamine Yamal, a Barcelona forward, posted “Hate me or love me, I don’t care” on Instagram after Spain beat England 3 to 2 in the UEFA Nations League at Wembley Stadium on Sept. 26, 2026. Yamal scored the opening goal of that match and then shared the…
Scientists, Regulators and Industry Convene at PCPC’s 2026 Science Symposium
This annual event offers two days of presentations from experts in their fields, coupled with insight from US regulatory bodies like the Food & Drug Administration (FDA) and the Environmental Protection Agency (EPA).
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Global Health & WASH: October 2026 Funding Opportunities (26 new opportunities)
41 opportunities on the Global Health & WASH page this cycle, 26 of them new. The health money on this page is mostly research money, and much of it is aimed at people early in their careers. Funder after funder, from the TANGO2 Research Foundation’s Research Pathways grants to the South African Medical Research Council’s Early Investigators Programme to the National Ataxia Foundation and Alex’s Lemonade Stand Foundation, is funding early-career investigators and the institutions that host them, with a clear tilt toward researchers based in or from the Global South, from AMBSO in Uganda to Kenya’s CONNECT-VAX network to Ersilia’s AI Incubator for African drug discovery. The money is organized by disease as much as by need: narrow foundations, the Parkinson’s Foundation, NTAP for neurofibromatosis, the RUNX1 Research Program for an inherited blood disorder, each run their own grant, so the question for an applicant is less which health problem you work on than which foundation owns your disease. The largest European cheques go to consortia only, with the European Innovation Council putting up to EUR €4 million behind a single healthy-ageing project and UNITE up to EUR €1 million behind each cross-border digital health team. Water sits apart, almost entirely in applied utility research: the Water Research Foundation’s cluster of USD $250,000 to $500,000 projects, each built around a utility problem from sewer laterals to reuse treatment. And the biggest single pot is public: HRSA is renewing community health center service areas with awards of up to USD $10 million each. This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Opportunities 2027 Building the Field of Health Advocacy Grant, Georgia Health Initiative. *New!* *Closing soon!* Georgia Health Initiative, a nonprofit private foundation focused on health equity, is opening its 2027 Building the Field of Health Advocacy Grant to strengthen the organizations that push for healthier systems across the state. The Initiative treats advocacy as infrastructure, backing one-year projects that range from urgent responses to shifting federal policy to longer arc campaigns on housing, behavioral health, care access, and food security. It wants coalitions that elevate community voice, apply an equity lens, and arrive ready to deploy concrete strategies, and it deliberately seeks variety in issues, communities, and organizational size. The optional webinar and required lobbying training signal a funder investing in a durable field, not one grant cycle. The strongest fits are Georgia nonprofits and public agencies ready to turn advocacy readiness into systems change. Geographies: United States. Who can apply: Tax-exempt 501(c)(3) public charities, state or local government agencies, or coalitions/collaboratives using a 501(c)(3) fiscal sponsor, that work in Georgia on behalf of Georgians and align with the mission, vision, and values of Georgia Health Initiative. Only one application per organization, though an organization may be listed as a partner or subcontractor on multiple applications. Funding amount: Organizations can apply for up to USD $80,000 per project; the Initiative anticipates investing a total of USD $1.36 million. Targeted Sectors / SDGs: Governance, Policy & Advocacy · SDG3; SDG10. Deadline: October 2, 2026. Learn more and apply here. TDD Research Pathways Grant, TANGO2 Research Foundation. *New!* *Closing soon!* The TANGO2 Research Foundation, working with a founding gift from Tango2UK, is opening its TDD Research Pathways Grant to fund early-career investigators anywhere in the world who study TANGO2 Deficiency Disorder. The foundation is using this money to seed pilot work and to build a research community around a rare disorder that few labs currently study. It wants to back emerging scientists, clinicians, and trainees who will stay with this field, not one-off projects, which is why it requires a committed mentor and real institutional support and asks each applicant for a plan for future contributions. The design rewards promise and persistence over track record. The strongest fits are students, residents, postdoctoral fellows, and new investigators ready to commit to TANGO2 research. Geographies: Global. Who can apply: Early-career investigators worldwide, including undergraduate, graduate, and medical trainees, postdoctoral fellows, residents, and clinical or research fellows. Early-career investigators must hold a PhD, MD, or equivalent doctoral degree and be within 10 years of that degree or within 5 years of initiating rare disease research, engaged in postdoctoral research at an academic institution, teaching hospital, or nonprofit research institute. Currently enrolled trainees and transitional PhD candidates beginning a postdoctoral appointment at a nonprofit institution by July 1, 2026 are also eligible. All applicants must identify a qualified mentor with relevant TDD or rare disease research experience and demonstrate institutional support. Funding amount: Up to USD $15,000. Targeted Sectors / SDGs: Innovation, Science & Technology. Deadline: October 2, 2026. Learn more and apply here. Call for Expression of Interest (EOI): AMBSO Impact Research Initiative, Africa Medical and Behavioral Sciences Organization (AMBSO). *Closing soon!* The Africa Medical and Behavioral Sciences Organization is inviting expressions of interest for its Impact Research Initiative, which funds early-stage pilot studies and proof of concept work on priority health challenges. AMBSO is a Ugandan research organization backing bold ideas that can grow into larger studies, and its tracks span antimicrobial resistance, epidemic preparedness, artificial intelligence for diagnostics, HIV prevention, climate and health, cancer, maternal and child health, and non-communicable diseases. The small award size and direct-cost-only rule point to a funder that wants evidence rather than overhead, and that favors projects able to attract follow-on money or use AMBSO’s own research infrastructure. Eligibility runs worldwide, welcoming researchers and institutions inside and outside Africa. The strongest fits are researchers with a feasible pilot that generates policy-ready evidence. Geographies: Global. Who can apply: Open worldwide to researchers, academic institutions, and research organizations whose work aligns with generating high-impact health evidence. Independent researchers are eligible if affiliated with an academic or research institution. Doctoral students are encouraged to apply as part of a research team led by a faculty Principal Investigator. Community-based organizations and other eligible entities may apply. Projects may be implemented inside or outside Uganda. Funding amount: Up to USD $10,000 per project, with no minimum funding threshold. Targeted Sectors / SDGs: SDG3. Deadline: October 2, 2026. Learn more and apply here. Entrepreneurs for Resilience 2027 (E4R27), Swiss Re Foundation. *Closing soon!* Swiss Re Foundation is opening the tenth edition of Entrepreneurs for Resilience, which backs healthcare social enterprises expanding affordable, quality primary care for low-income communities across low- and middle-income countries. The Foundation is using catalytic, blended funding plus multi-year advisory to take risk out of scaling, helping proven models reach more people sustainably rather than stay stuck at pilot size. It looks to back brick-and-click enterprises that pair physical care with digital tools and build affordability through savings, payments, or microinsurance, ideally integrated with public health systems. Requiring earned revenue, real customer scale, and gender-inclusive teams signals that this is growth capital for commercially serious ventures, not seed money. The strongest fits are established, revenue-generating healthcare enterprises ready to scale access for underserved populations. Geographies: Global. Who can apply: For-profit and hybrid healthcare social enterprises serving low-income and underserved populations in low- and middle-income countries, using combined physical and digital (brick-and-click) healthcare delivery models. Applicants must demonstrate measurable social impact, generate at least 50% of income through earned revenue, have been established no later than December 2023, already serve at least 50,000 people with potential to scale, and have at least 30% low-income customers. Preference for gender-inclusive enterprises with at least one female founder or at least 30% women in management. Funding amount: Pilot funding of up to USD $45,000 for the 6-8 shortlisted social enterprises; up to USD $1 million in blended funding (up to USD $600,000 unconditional plus up to USD $400,000 conditional) shared among 3-4 finalists over a two-year implementation period; total programme funding approximately USD $1.3 million. Targeted Sectors / SDGs: Innovation, Science & Technology · SDG3; SDG5. Deadline: October 4, 2026. Learn more and apply here. Horizon HealthTech 2026, IIITD Innovation & Incubation Center. *Closing soon!* IIITD Innovation and Incubation Center is opening Horizon HealthTech 2026, a nine-month MedTech incubation cohort run under the aegis of BIRAC EDGE for founders building medical devices, diagnostics, surgical and interventional tools, or remote monitoring. The center is backing teams that already hold a validated proof of concept and want to reach a tested prototype, pairing each founder with a named expert and clinical validation through a hospital partner. What they seek to back is Indian health technology that can survive real clinical scrutiny and regulatory review, not slideware. The structure, with lab access, CDSCO guidance, IP support, and investor connects, signals a program built to make startups ready for follow-on capital. The strongest fits are early-stage MedTech teams with defensible IP and a working path from bench to bedside. Geographies: India. Who can apply: Early-stage teams/startups with a validated idea/proof of concept (TRL 3 onwards) working on medical devices, diagnostics, surgical/interventional technologies, or remote monitoring, holding IP or with potential to generate IP. Funding amount: No cash award amount is stated on the official program page. Benefits are in-kind: prototyping lab and equipment access, clinical/hospital validation (Max Healthcare), expert mentorship, CDSCO regulatory guidance, IP filing support, workspace, cloud credits and tools, and investor connects. Run under the aegis of BIRAC EDGE and builds a path to follow-on Government/VC/Angel funding. Targeted Sectors / SDGs: Innovation, Science & Technology · SDG3; SDG9. Deadline: October 5, 2026. Learn more and apply here. OpenAI AI and Teen Development Research Grant Program, OpenAI. *New!* *Closing soon!* OpenAI is opening its AI and Teen Development Research Grant Program to fund independent studies of how generative AI shapes the lives of young people ages 13 to 17. OpenAI wants a stronger evidence base on how teens actually use these tools and what that use does to their social and emotional development, so that product teams, policymakers, and regulators can act on findings rather than assumptions. It seeks work on emotion regulation, relationships, belonging, and the safeguards that keep teens safer, welcoming interdisciplinary teams and any methodology. That it will fund research whose conclusions may not favor AI providers, and prioritizes studies touching ChatGPT, signals a genuine appetite for credible outside scrutiny. The strongest fits are research groups grounded in adolescent development, well-being, and AI safety. Geographies: Global. Who can apply: Researchers aged 18 or older who are affiliated with a research institution or organization, and/or have significant experience relevant to child or adolescent development, well-being, human-computer interaction, AI impacts, or a closely related field. Multiple institutions may collaborate. OpenAI is seeking to fund research rather than for-profit initiatives and will not prioritize for-profit organizations. Funding amount: Individual research grants up to USD $1 million, totaling up to USD $5 million; maximum 10% of each grant for overhead or indirect costs. Targeted Sectors / SDGs: Social Inclusion & Community Wellbeing; Innovation, Science & Technology. Deadline: October 6, 2026. Learn more and apply here.