Paper by Claudio M Radaelli: “How can science have more impact on policy decisions? The P-Cube Project has approached this question by creating five pedagogical computer games based on missions given to a policy entrepreneur (the player) advocating for science-informed policy decisions. The player explores simplified strategies for policy change rooted in a small number of variables, thus making it possible to learn without a prior background in political science or public administration. The games evolved from the intuition that, instead of making additional efforts to explain science to decision-makers, we should directly empower would-be scientists (our primary audience for the games), post-graduates in public policy and administration, and activists for science. The two design principles of the games revolve around learning about how policy decisions are made (a learning-about-content principle) and reflection. Indeed, the presence of science in the policy process raises ethical and normative decisions, especially when we consider controversial strategies like civil disobedience and alliances with industry. To be on the side of science does not mean to be outside society and politics. I show the motivation, principles, scripts and pilots of the science games, reflecting on how they can be used and for what reasons…(More)”
Entering the Vortex
Essay by Nils Gilman: “A strange and unsettling weather pattern is forming over the landscape of scholarly research. For decades, the climate of academic inquiry was shaped by a prevailing high-pressure system, a consensus grounded in the vision articulated by Vannevar Bush in “Science: The Endless Frontier” (1945). That era was characterized by robust federal investment, a faith in the university as the engine of basic research, and a compact that traded public funding for scientific autonomy and the promise of long-term societal benefit. It was a climate conducive to the slow, deliberate, and often unpredictable growth of knowledge, nurtured by a diverse ecosystem of human researchers — the vital “seed stock” of intellectual discovery.
But that high-pressure system is collapsing. A brutal, unyielding cold front of academic defunding has swept across the nation, a consequence of shifting political priorities, populist resentment, and a calculated assault on the university as an institution perceived as hostile to certain political agendas. This is not merely a belt-tightening exercise; it is, for all intents and purposes, the dismantling of Vannevar Bush’s Compact, the end of the era of “big government”-funded Wissenschaft. Funding streams for basic research are dwindling, grant applications face increasingly long odds, and the financial precarity of academic careers deters the brightest minds. The human capital necessary for sustained, fundamental inquiry is beginning to wither.
Simultaneously, a warm, moisture-laden airmass is rapidly advancing: the astonishing rise of AI-based research tools. Powered by vast datasets and sophisticated algorithms, these tools promise to revolutionize every stage of the research process – from literature review and data analysis to hypothesis generation and the drafting of scholarly texts. As a recent New Yorker piece on AI and the humanities suggests, these AI engines can already generate deep research and coherent texts on virtually any subject, seemingly within moments. They offer the prospect of unprecedented efficiency, speed, and scale in the production of scholarly output.
The collision of these two epochal weather systems — the brutal cold front of academic defunding and the warm, expansive airmass of AI-based research tools — is creating an atmospheric instability unlike anything the world of scholarship has ever witnessed. Along the front where these forces meet, a series of powerful and unpredictable tornados are beginning to touch down, reshaping the terrain of knowledge production in real-time…(More)”.
Real-time prices, real results: comparing crowdsourcing, AI, and traditional data collection
Article by Julius Adewopo, Bo Andree, Zacharey Carmichael, Steve Penson, Kamwoo Lee: “Timely, high-quality food price data is essential for shock responsive decision-making. However, in many low- and middle-income countries, such data is often delayed, limited in geographic coverage, or unavailable due to operational constraints. Traditional price monitoring, which relies on structured surveys conducted by trained enumerators, is often constrained by challenges related to cost, frequency, and reach.
To help overcome these limitations, the World Bank launched the Real-Time Prices (RTP) data platform. This effort provides monthly price data using a machine learning framework. The models combine survey results with predictions derived from observations in nearby markets and related commodities. This approach helps fill gaps in local price data across a basket of goods, enabling real-time monitoring of inflation dynamics even when survey data is incomplete or irregular.
In parallel, new approaches—such as citizen-submitted (crowdsourced) data—are being explored to complement conventional data collection methods. These crowdsourced data were recently published in a Nature Scientific Data paper. While the adoption of these innovations is accelerating, maintaining trust requires rigorous validation.
A newly published study in PLOS compares the two emerging methods with the traditional, enumerator-led gold standard, providing new evidence that both crowdsourced and AI-imputed prices can serve as credible, timely alternatives to traditional ground-truth data collection—especially in contexts where conventional methods face limitations…(More)”.
These Startups Are Building Advanced AI Models Without Data Centers
Article by Will Knight: “Researchers have trained a new kind of large language model (LLM) using GPUs dotted across the world and fed private as well as public data—a move that suggests that the dominant way of building artificial intelligence could be disrupted.
Article by Will Knight: “Flower AI and Vana, two startups pursuing unconventional approaches to building AI, worked together to create the new model, called Collective-1.
Flower created techniques that allow training to be spread across hundreds of computers connected over the internet. The company’s technology is already used by some firms to train AI models without needing to pool compute resources or data. Vana provided sources of data including private messages from X, Reddit, and Telegram.
Collective-1 is small by modern standards, with 7 billion parameters—values that combine to give the model its abilities—compared to hundreds of billions for today’s most advanced models, such as those that power programs like ChatGPT, Claude, and Gemini.
Nic Lane, a computer scientist at the University of Cambridge and cofounder of Flower AI, says that the distributed approach promises to scale far beyond the size of Collective-1. Lane adds that Flower AI is partway through training a model with 30 billion parameters using conventional data, and plans to train another model with 100 billion parameters—close to the size offered by industry leaders—later this year. “It could really change the way everyone thinks about AI, so we’re chasing this pretty hard,” Lane says. He says the startup is also incorporating images and audio into training to create multimodal models.
Distributed model-building could also unsettle the power dynamics that have shaped the AI industry…(More)”
In Uncertain Times, Get Curious
Chapter (and book) by Elizabeth Weingarten: “Questions flow from curiosity. If we want to live and love the questions of our lives—How to live a life of purpose? Who am I in the aftermath of a big change or transition? What kind of person do I want to become as I grow older?—we must first ask them into conscious existence.
Many people have written entire books defining and redefining curiosity. But for me, the most helpful definition comes from a philosophy professor, Perry Zurn, and a systems neuroscientist, Dani Bassett: “For too long—and still too often—curiosity has been oversimplified,” they write, typically “reduced to the simple act of raising a hand or voicing a question, especially from behind a desk or a podium. . . . Scholars generally boil it down to ‘information-seeking’ behavior or a ‘desire to know.’ But curiosity is more than a feeling and certainly more than an act. And curiosity is always more than a single move or a single question.”Curiosity works, they write, by “linking ideas, facts, perceptions, sensations and data points together.”It is complex, mutating, unpredictable, and transformational. It is, fundamentally, an act of connection, an act of creating relationships between ideas and people. Asking questions then, becoming curious, is not just about wanting to find the answer—it is also about our need to connect, with ourselves, with others, with the world.
And this, perhaps, is why our deeper questions are hardly ever satisfied by Google or by fast, easy answers from the people I refer to as the Charlatans of Certainty—the gurus, influencers, and “experts” peddling simple solutions to all the complex problems you face. This is also the reason there is no one-size-fits-all formula for cultivating curiosity—particularly the kind that allows us to live and love our questions, especially the questions that are hard to love, like “How can I live with chronic pain?” or “How do I extricate myself from a challenging relationship?” This kind of curiosity is a special flavor…(More)”. See also: Inquiry as Infrastructure: Defining Good Questions in the Age of Data and AI.
Mapping local knowledge supports science and stewardship
Paper by Sarah C. Risley, Melissa L. Britsch, Joshua S. Stoll & Heather M. Leslie: “Coastal marine social–ecological systems are experiencing rapid change. Yet, many coastal communities are challenged by incomplete data to inform collaborative research and stewardship. We investigated the role of participatory mapping of local knowledge in addressing these challenges. We used participatory mapping and semi-structured interviews to document local knowledge in two focal social–ecological systems in Maine, USA. By co-producing fine-scale characterizations of coastal marine social–ecological systems, highlighting local questions and needs, and generating locally relevant hypotheses on system change, our research demonstrates how participatory mapping and local knowledge can enhance decision-making capacity in collaborative research and stewardship. The results of this study directly informed a collaborative research project to document changes in multiple shellfish species, shellfish predators, and shellfish harvester behavior and other human activities. This research demonstrates that local knowledge can be a keystone component of collaborative social–ecological systems research and community-lead environmental stewardship…(More)”.
Inquiry as Infrastructure: Defining Good Questions in the Age of Data and AI
Paper by Stefaan Verhulst: “The most consequential failures in data-driven policymaking and AI deployment often stem not from poor models or inadequate datasets but from poorly framed questions. This paper centers question literacy as a critical yet underdeveloped competency in the data and policy landscape. Arguing for a “new science of questions,” it explores what constitutes a good question-one that is not only technically feasible but also ethically grounded, socially legitimate, and aligned with real-world needs. Drawing on insights from The GovLab’s 100 Questions Initiative, the paper develops a taxonomy of question types-descriptive, diagnostic, predictive, and prescriptive-and identifies five essential criteria for question quality: questions must be general yet concrete, co-designed with affected communities and domain experts, purpose-driven and ethically sound, grounded in data and technical realities, and capable of evolving through iterative refinement. The paper also outlines common pathologies of bad questions, such as vague formulation, biased framing, and solution-first thinking. Rather than treating questions as incidental to analysis, it argues for institutionalizing deliberate question design through tools like Q-Labs, question maturity models, and new professional roles for data stewards. Ultimately, the paper contends that the questions are infrastructures of meaning. What we ask shapes not only what data we collect or what models we build but also what values we uphold and what futures we make possible…(More)”.
Open with care: transparency and data sharing in civically engaged research
Paper by Ankushi Mitra: “Research transparency and data access are considered increasingly important for advancing research credibility, cumulative learning, and discovery. However, debates persist about how to define and achieve these goals across diverse forms of inquiry. This article intervenes in these debates, arguing that the participants and communities with whom scholars work are active stakeholders in science, and thus have a range of rights, interests, and researcher obligations to them in the practice of transparency and openness. Drawing on civically engaged research and related approaches that advocate for subjects of inquiry to more actively shape its process and share in its benefits, I outline a broader vision of research openness not only as a matter of peer scrutiny among scholars or a top-down exercise in compliance, but rather as a space for engaging and maximizing opportunities for all stakeholders in research. Accordingly, this article provides an ethical and practical framework for broadening transparency, accessibility, and data-sharing and benefit-sharing in research. It promotes movement beyond open science to a more inclusive and socially responsive science anchored in a larger ethical commitment: that the pursuit of knowledge be accountable and its benefits made accessible to the citizens and communities who make it possible…(More)”.
Who Owns Science?
Article by Lisa Margonelli: “Only a few months into 2025, the scientific enterprise is reeling from a series of shocks—mass firings of the scientific workforce across federal agencies, cuts to federal research budgets, threats to indirect costs for university research, proposals to tax endowments, termination of federal science advisory committees, and research funds to prominent universities held hostage over political conditions. Amid all this, the public has not shown much outrage at—or even interest in—the dismantling of the national research project that they’ve been bankrolling for the past 75 years.
Some evidence of a disconnect from the scientific establishment was visible in confirmation hearings of administration appointees. During his Senate nomination hearing to head the department of Health and Human Services, Robert F. Kennedy Jr. promised a reorientation of research from infectious disease toward chronic conditions, along with “radical transparency” to rebuild trust in science. While his fans applauded, he insisted that he was not anti-vaccine, declaring, “I am pro-safety.”
But lack of public reaction to funding cuts need not be pinned on distrust of science; it could simply be that few citizens see the $200-billion-per-year, envy-of-the-world scientific enterprise as their own. On March 15, Alabama meteorologist James Spann took to Facebook to narrate the approach of 16 tornadoes in the state, taking note that people didn’t seem to care about the president’s threat to close the National Weather Service. “People say, ‘Well, if they shut it down, I’ll just use my app,’” Spann told Inside Climate News. “Well, where do you think the information on your app comes from? It comes from computer model output that’s run by the National Weather Service.” The public has paid for those models for generations, but only a die-hard weather nerd can find the acronyms for the weather models that signal that investment on these apps…(More)”.
Inside arXiv—the Most Transformative Platform in All of Science
Article by Sheon Han: “Nearly 35 years ago, Ginsparg created arXiv, a digital repository where researchers could share their latest findings—before those findings had been systematically reviewed or verified. Visit arXiv.org today (it’s pronounced like “archive”) and you’ll still see its old-school Web 1.0 design, featuring a red banner and the seal of Cornell University, the platform’s institutional home. But arXiv’s unassuming facade belies the tectonic reconfiguration it set off in the scientific community. If arXiv were to stop functioning, scientists from every corner of the planet would suffer an immediate and profound disruption. “Everybody in math and physics uses it,” Scott Aaronson, a computer scientist at the University of Texas at Austin, told me. “I scan it every night.”
Every industry has certain problems universally acknowledged as broken: insurance in health care, licensing in music, standardized testing in education, tipping in the restaurant business. In academia, it’s publishing. Academic publishing is dominated by for-profit giants like Elsevier and Springer. Calling their practice a form of thuggery isn’t so much an insult as an economic observation. Imagine if a book publisher demanded that authors write books for free and, instead of employing in-house editors, relied on other authors to edit those books, also for free. And not only that: The final product was then sold at prohibitively expensive prices to ordinary readers, and institutions were forced to pay exorbitant fees for access…(More)”.