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This week on Power & Politics: Cornell latest, Tax Hike proposals, RAISE Act breakdown
Arizona State Commissions Outside Investigator to Review Hockey Workout
Coach Greg Powers was placed on leave earlier this week.
Apple Watch Ultra 4 is an AI Powerhouse With Fitness and Health Improvements, But Do You Need it?
Apple has upgraded the Ultra again, and there's lots to like in the new version, including a super-accurate HR sensor and a powerful new AI ready chip.
Health officials alarmed by increased measles cases amid proposals to lower vaccine mandates in Florida
Health officials in Florida are ringing the alarm as confirmed measles cases continue to explode across the state.
IRS takes up whistleblower claim of Hope Florida money that flowed to Uthmeier’s political committee
The Internal Revenue Service is reviewing a whistleblower claim about $10 million in Medicaid settlement money that moved through the Hope Florida Foundation and, within weeks, into a political committee chaired by James Uthmeier, now Florida’s attorney general, according to new records obtained by the Florida Trident.
Zay Flowers listed as questionable
Ravens WR Zay Flowers questionable after a limited practice Friday
Fiery crowd caps Mississippi redistricting hearings. Opponents urge: ‘Leave our maps alone’
Tempers flared Thursday at a packed redistricting hearing at the Mississippi Capitol, where a large majority of speakers criticized what they said was a power grab aimed at diminishing Black political power and further entrenching Republican dominance over state politics.
ASUS ROG Edition 20 - Celebrating 20 years of Republic of Gamers
ASUS is celebrating 20 years, with its "Edition 20" products that are pure enthusiast excess in the best possible way, including a custom AMD Ryzen X870E board & AIO, GeForce RTX 5090, 3000 W PSU, open-frame case and pairing it with a high-end OLED into one beautiful showcase.
Fairport Harbor downtown businesses band together for community night out
The annual Witches Night Out event is back with more planned for this weekend, including an opportunity to check out a special "haunted" lighthouse tower, but beware of the ghost cat!
Richard Hightower on Sam Roush's struggles on Bears special teams
Bears special teams coordinator Richard Hightower reacted to rookie tight end Sam Roush's struggles on special teams this season.
At $1.30 a share, Fin Posillipo buys depositary shares in Genenta Science (Nasdaq: GNTA).
Reporting persons may be deemed to own about 11.5% of shares and 18.94% of voting power. Fin Posillipo bought 110,000 ADSs August 7–19, 2026.
Tigerlily Foundation's Pajama Glam Empowers Girls and Families Through Health Literacy, Self-Advocacy and Connection
/PRNewswire/ -- What began with a young girl imagining a different kind of breast cancer gathering has grown into a joyful, multigenerational health and...
Texas Tenors set to open Entertainment Series at the Whatley Center on October 20
September 29, 2026 Three powerhouse voices spanning country, classical, Broadway and pop will take the Whatley Center stage when The Texas Tenors perform Tuesday, Oct. 20, at 7:30 p.m. The Texas Tenors — JC Fisher, Marcus Collins and John Hagen — first gained national attention as finalists on NBC’s America’s Got Talent in 2009. Since then,…
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. 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Are sports & entertainment becoming the 'anti-AI trade'?
Turney Duff chats with Yahoo Sports staff writer Dylan Dittrich and Clocktower Group chief macro strategist Eric Wallerstein about how sports and the "entertainment economy" are emerging as attractive investments as the public's appetite for in-person experiences continues to grow.
Kentucky Wesleyan to present Ellie Magnuson Lecture in Literature and Science: “The Power of Still Becoming: Using Reflection and Writing to Evolve”
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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
New Mexico group seeks to ban all corporate, ‘dark’ money spending on politics
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Your Weekly Michigan Political Brief
The Detroit Free Press endorsed Democratic U.S. Senate nominee Abdul El-Sayed. He also received endorsements this week from Sierra Club Michigan and the Michigan Laborers’ International Union of North America, or LiUNA. Republican U.S. Senate nominee Mike Rogers was endorsed by the Michigan Corn Growers Association. The Michigan Professional Firefighters Union and National Security Leaders […]
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Today marks the first day of Rut Watch 2026 With another season of Deer Spy in the books thanks to community scientists like you, biologists are now on a mission to pinpoint the peak of the white-tailed deer breeding season in Maine. Every observation of deer breeding behavior and sign helps define the timing of the rut, and this project uses the power of community science to collect deer data all fall long. Hunters, hikers, and wildlife watchers - Rut Watch pairs well with any outdoor adventure you have planned this season. It’s a perfect excuse to slow down and connect with nature as you look for clues. So get outside, rack up that deer data and submit rut reports from now through December 1. The online form is science made simple: Location Date and time Breeding behavior observed Submit a Rut Report What to watch for Active Breeding The most obvious breeding behavior to observe is a buck mounting a doe, but that is a rare sight as it only lasts a moment. You are more likely to witness a buck “tending” a doe, following closely, chasing, nuzzling, or pawing at her rump until she is receptive. Sparring After bucks shed their velvet, sparring helps sort out who is who in the dominance hierarchy. Early season matches are often less intense than later season matches between similar sized bucks. Most sparring is complete by the time breeding begins. Rubs Don’t just look for deer, pay attention to the trees. Rubs are marks made by bucks when they rub their antlers on trees, removing bark and leaving their scent calling card in the process. Scrapes Look to the forest floor for this sign of breeding season behavior. Scrapes consist of two parts - a licking branch, and a patch of cleared earth. Bucks break a low branch around head height, then paw at the leaves below to expose the dirt. Covered in scent, a scrape tells does and other bucks, “I’m here!” Learn More More information What’s next for deer community science: Northern Maine Wintering Deer Survey Other active community science projects: Fish Tag Reporting Maine Golden Eagle Study Maine Amphibian & Reptile Atlas Project Maine Deer Management: Maine’s Big Game Management Plan Game Species Conservation & Management Report The Benefits of Hunting: Supporting Wildlife, Conservation, and Community Subscribe Share Leave a comment
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How Washington Funded the Politicization of Science
Over the last half-decade, the National Science Foundation (NSF) has spent hundreds of millions of dollars on projects designed to influence academic policies, both in the sciences and other disciplines. The aim was to foster a progressive conception of social justice in these fields. In pursuit of this end, the foundation earmarked nearly a million dollars for a project titled “Creating Inclusive Scientific Societies through Policies and Practices,” over a million for a project on “Faculty Online Learning Communities for Gender Equity,” and over $2 million to foster “racial equity focused departmental change.” Through a public records request, I’ve obtained the proposals and progress reports for dozens of NSF grants designed to promote social justice. Although funding for these grants was eventually halted by the Trump administration, the projects illustrate an important facet of recent higher-education policy. Many of the university policies that promised “social transformation” didn’t arise in a vacuum. They were actively encouraged by outside funders—often by the federal government itself. Subscribe Accreditation—the peer-review process which enables universities to receive federal grants and student loans—has recently become a policy battleground, with accreditors wielding significant power over both universities and academic disciplines. In 2020, when Oregon Health and Science University’s medical school issued an extensive diversity action plan—which promised “consequences” for employees who failed to support “DEI, anti-racism and social justice”—it specifically pointed to the Liaison Committee on Medical Education (LCME), the medical-school accrediting body, to justify the overhaul. The NSF actively promoted DEI-focused accreditation policies. The proposal for a $1.2 million program on promoting “community engagement” in STEM, which I obtained through a records request, notes that the Accreditation Board for Engineering and Technology was exploring changes to its criteria “that would incorporate JEDI [justice, equity, diversity, and inclusion].” The proposal promised to “support broader JEDI efforts in the general criteria for engineering.” Another grant funded a conference designed to “identify and share best practices and strategies to integrate IDEA principles in accreditation criteria”—referring to an acronym that stands for “inclusion, diversity, equity, and accessibility.” Scholarly societies have become another flashpoint in debates over higher education. Critics argue that many of these societies, which have the power to shape entire disciplines, have become increasingly politicized. Again, through its giving, the NSF played a role in promoting this politicization. In one million-dollar grant that received funding in 2024, a group of psychologists proposed a series of social-justice reforms to the Society for the Psychological Study of Social Issues. The proposal, which I obtained via a records request, asserts that “[r]edistributing resources (such as moving different people into leadership roles) is not enough” and that the society’s “governance structures do not yet reflect its stated values of diversity, equity, and inclusion.” Its solution: review and rework the society’s criteria for leadership development, academic awards, conferences, and publications. Adjusting review criteria informed by “intersectionality” designed to fight “epistemic exclusion” is an easy way to introduce de facto ideological litmus tests into the peer-review process. For many of the grants I obtained, such litmus tests seem to be the point. “Academic departments are racialized social systems that produce and sustain the byproducts of systemic racism,” a proposal for a $2.7 million project funded in 2024 asserts, promising to develop “departmentally-focused change efforts that cultivate values, assumptions, and behaviors that advance racial equity.” The University of Illinois Urbana Champaign’s million-dollar “I-ADVANCE” project, which effectively created a small DEI bureaucracy on campus, promised to Embed 28 ADVANCE faculty fellows in STEM departments” to amplify its equity goals. Another $1.2 million project promised to create “faculty online learning communities,” made up of scientists at universities around the country, that would eventually build department-level diversity action plans. “Ultimately,” the proposal asserts, perhaps more perceptively than its authors realized, “both organizational change and intersectionality frameworks deal with power.” Indeed, such grants confer a great deal of power to administrators and scholar-activists, who can even use the mere availability of funding as a tool to advance their causes. That history is worth remembering amid today’s debate over federal involvement in higher education: Washington has been shaping university priorities for years, including by financing efforts to instill progressive ideas about race, gender, and equity into academic institutions—and many of the policies those grants encouraged remain embedded within universities long after the federal money has stopped flowing. John D. Sailer is the director of higher education policy and a senior fellow at Manhattan Institute. Share
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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.
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In late August, Texas and Florida became the latest states to limit the use of Flock cameras, an artificial intelligence-powered network of automated traffic surveillance devices, in response to growing
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Anxieties about the future often become refracted as questions of community identity: Who are we? What do we stand for?
Light Science Technologies Lowers 2026 Revenue Outlook Following Project Delays
Light Science Technologies Holdings (LSE:LST) expects full-year 2026 revenue of between £9. 0 million and £9.
Australian businesses raise prices as card surcharge ban takes effect
SYDNEY, Oct 1 (Reuters) - Australian businesses said a ban on card surcharges that took effect on Thursday would force them to raise prices, casting doubt on government claims the change would lower living costs by eliminating more than $1 billion in annual card fees.
Those Darn Accordions bring accordion-powered rock to Nevada City
Think you know what an accordion sounds like? Think again.
Jordan Brings Power and Speed to Giants’ Fall League Group
Dakota Jordan enters the Arizona Fall League after producing 19 home runs and 21 stolen bases in 2026.
Should the Reds Bring Eugenio Suárez Back for 2027?
Nostalgia clashes with production as Cincinnati weighs the veteran slugger's future. With younger talent waiting, the front office must decide if power justifies a costly reunion.
Alexandria's business community gathers as AEDP tallies first results of ALX Forward
The economic development group announced a $200,000 state grant at its annual meeting, where the developer of the Old Town North power plant site said it expects to break ground next year
How The Golden State Valkyries Built A WNBA Business Powerhouse
The Golden State Valkyries built a WNBA business powerhouse in two seasons, driving record attendance, 12,000+ season ticket holders and a No. 2 playoff seed.
Business Community Signals Openness to Removing Public Vote on Nuclear
PLYMOUTH — Gov. Maura Healey’s mission to restore nuclear power to Massachusetts has put the local business community at odds with nuclear watchdog groups. Advocates worry that the removal of […]
Aston Martin’s DBX GT Bets Big On Luxury And Power
The luxurious and powerful Aston Martin DBX GT sprints to 60 mph in just 3.1 seconds
Aston Martin's New DBX Shows Its Softer Side
The 2027 DBX GT gets a more comfortable ride, a quieter cabin, and upgraded seats without giving up the twin-turbo V8’s 717 horsepower.
Limited practice for Zay Flowers
Ravens WR Zay Flowers listed as limited Wednesday because of hamstring issue