The article, authored by a team spanning Harvard Kennedy School, New America, Minnesota Management and Budget and ESADE Business School, introduces a unified framework built on three sequential questions. Is the evidence useful, meaning does it speak to a problem policymakers actually face? Is it usable, meaning can decision-makers realistically interpret and act on it within their institutional constraints? And is it used, meaning does it ultimately change what governments do? The authors contend that a substantial production–adoption gap has opened because researchers and funders have concentrated overwhelmingly on the first stage, producing more and better studies, while paying far less systematic attention to the second and third stages where most evidence quietly dies.

The scale of the problem is striking when laid out in the literature the authors synthesize. Implementation science has estimated that it takes an average of seventeen years for research evidence to change clinical practice, and there is little reason to believe policy moves faster. Systematic reviews of barriers to evidence use among policymakers consistently identify the same culprits: poor timeliness, irrelevant framing, lack of trust, and absence of intermediaries who can translate findings into actionable recommendations. Meanwhile, surveys of civil servants in the United States and Australia reveal that many public employees have never encountered the evidence base relevant to their portfolios, and that those who have often lack the statistical training to interpret it.

One of the most provocative threads in the new framework concerns the psychology of the people on the receiving end. Policymakers, like everyone else, are boundedly rational and susceptible to motivated reasoning. Experimental work shows that politicians often update their beliefs selectively, discounting evidence that threatens their ideological commitments, and that they appear more resistant to debiasing interventions than the general public. Field experiments with local officials across multiple countries have tested whether presenting impact estimates changes behaviour, with mixed results. The authors argue that this growing body of behavioural evidence on evidence reception remains fragmented across political science, economics and psychology, and that integrating it is essential to understanding why even high-quality, well-communicated findings go unused.

The framework also draws attention to the supply side of the problem: the characteristics of the evidence itself. Credibility matters, a finding that stretches back decades in communication research, but so do practical attributes such as cost, feasibility and the political palatability of the intervention being tested. Conjoint experiments with US state government officials suggest that decision-makers weigh implementation difficulty and price alongside effect sizes, sometimes favouring a less effective but easier-to-run programme over a more effective but operationally demanding one. Studies of policy diffusion show that effectiveness information travels unevenly across jurisdictions, and that ideological alignment between the source of evidence and the adopting government can matter as much as the underlying results, a pattern documented in recent work on evidence-based policy adoption.

Intermediary organizations occupy a central place in the agenda. The United Kingdom’s What Works Centres, evidence clearinghouses in the United States, and a growing ecosystem of knowledge brokers all exist to bridge the production–adoption gap, yet the authors note that scholarship on these intermediaries is surprisingly thin. Are they neutral boundary organizations or politically savvy policy entrepreneurs? Which brokering strategies, skills and organizational forms actually increase uptake? Large-scale studies using tools such as Altmetric and Overton to trace the societal impact of policy research are beginning to provide answers, and the authors highlight emerging work on AI-augmented clearinghouses and large language models for evidence synthesis as potentially transformative, if unproven, technologies for making evidence cheaper to find and digest.

Scaling presents another underexplored bottleneck. A randomized evaluation conducted in one context may not transport to another, and formal work on the transportability of experimental results remains largely confined to technical journals. Evidence from two prominent government nudge units suggests that effect sizes observed in academic settings often shrink when interventions are deployed at administrative scale, and megastudies involving hundreds of thousands of participants have revealed substantial heterogeneity in what works. The authors argue that the field lacks a systematic understanding of when and why findings generalize, and that policymakers’ justified scepticism about external validity is rarely treated as a researchable question in its own right rather than an obstacle to be overcome with better messaging.

Organizational learning offers the final pillar of the framework. Governments are not individual decision-makers but complex bureaucracies with routines, incentives, capacities and cultures that shape whether information is absorbed. Research on organizational learning, drawn heavily from the private sector, suggests that learning requires deliberate structures: feedback loops, protected time for reflection, and leadership that rewards experimentation. Public sector studies indicate that involvement in performance management routines can increase the use of performance information, but that public managers may be more risk-averse than private counterparts, and that status quo bias systematically dampens appetite for reform. The authors call for research that treats evidence adoption as an organizational capability to be built, not merely a message to be delivered.

What emerges from the synthesis is a concrete research agenda rather than a lament. The authors identify gaps at every stage: we know little about how policymakers search for evidence in the first place, how they trade off competing studies, how intermediary organizations should be designed, how to predict transportability, and how organizational contexts amplify or suppress uptake. They argue that closing these gaps is not an academic luxury. Billions of dollars in social spending ride on decisions made with thin or stale evidence, and the promise of the evidence-based policymaking movement, improving societal outcomes through better decisions, cannot be kept if the last mile between journal and legislation remains unmapped. The Perspective, which grew out of collaboration between academics and practitioners inside government, models the kind of co-production it recommends: research questions shaped by the people who must actually use the answers.

Subject of Research: A research agenda for closing the gap between the production of policy-relevant research and its adoption in government policymaking.

Article Title: A research agenda for making evidence useful, usable and used in policymaking

Article References: A research agenda for making evidence useful, usable and used in policymaking. (n.d.). https://doi.org/10.1038/s41562-026-02593-3

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02593-3

Keywords: evidence-based policymaking, Nature Human Behaviour, production-adoption gap, behavioural public policy, knowledge brokers, organizational learning, policy diffusion, implementation science, What Works Centres, randomized evaluations, policymaker decision-making, research translation

A fundamental obstacle in AI policy arises from the mutable nature of what constitutes valid evidence. The criteria for credibility vary dramatically across diverse application domains and societal contexts. For instance, experiments demonstrating AI safety in controlled lab environments may not capture the complexity of real-world deployment, where socio-technical factors, user interactions, and unforeseen emergent behaviors come into play. This ambiguity in defining “solid evidence” introduces a tension between premature regulation—risking stifling innovation—and regulatory inertia, which can leave society exposed to unchecked harms.

Bommasani et al. emphasize that this dilemma necessitates governance architectures capable of evolving in tandem with emerging scientific insights. They envision dynamic policy ecosystems, where regulations are not static edicts but adjustable frameworks responsive to new data and methodologies. In practice, this means embedding mechanisms for ongoing model assessment, rigorous pre-release evaluations, and transparent disclosure of safety protocols throughout the AI lifecycle. Such adaptive strategies would mitigate risks while preserving the incentives necessary for technological advancement.

One of the central tenets proposed involves incentivizing thorough pre-deployment evaluations of AI systems. These evaluations should incorporate stress testing across diverse scenarios, including adversarial conditions and worst-case usage patterns. By instituting standardized benchmarks and validation protocols, policymakers can foster a culture of accountability among AI developers while generating reproducible evidence on system robustness and failure modes. This approach aligns with practices in other high-stakes sectors, such as pharmaceuticals, where rigorous clinical trials precede market release.

Transparency emerges as another crucial pillar underpinning evidence-based governance. Bommasani and collaborators advocate for policies that mandate public disclosure of safety practices and performance metrics. Enhanced transparency serves multiple functions: it empowers independent researchers to audit and verify claims, enables affected communities to make informed decisions, and cultivates public trust in AI technologies. In addition, transparent practices help illuminate blind spots and biases, ensuring that AI systems do not perpetuate social inequities or systemic risks.

Crucially, the authors highlight the importance of establishing robust monitoring infrastructures to detect and address harms following deployment. Even the most comprehensive pre-release evaluations cannot anticipate all potential adverse effects. Post-deployment surveillance systems, potentially leveraging digital trace data and real-time feedback loops, can identify emergent harms—ranging from algorithmic discrimination to manipulative content generation. Effective monitoring necessitates coordination across governmental agencies, research institutions, industry stakeholders, and civil society groups.

A vital enabler of this evidence ecosystem is the protection and empowerment of independent researchers. Bommasani et al. propose the introduction of safe harbor provisions that shield these researchers from legal and proprietary risks when conducting critical evaluations or exposing vulnerabilities. Such protections are indispensable to expanding the evidentiary base and fostering a culture of open inquiry that challenges corporate narratives and governmental complacency. Independent audits and third-party assessments serve as essential counterbalances within a democratic governance framework.

Beyond technical evaluations, the article stresses the necessity of situating AI within a broader socio-technical context. AI systems do not operate in isolation; they interact with existing social, economic, and political structures in complex and often unpredictable ways. Accordingly, policy interventions must be grounded not only in technical evidence but also in interdisciplinary research encompassing ethics, sociology, economics, and law. Crafting policies informed by a holistic evidence base amplifies the likelihood of equitable and effective governance outcomes.

Fostering expert consensus remains a linchpin for navigating uncertainty and disagreement within the AI policy landscape. The authors envision convening credible, inclusive scientific bodies that integrate diverse expertise and perspectives. These bodies would synthesize emerging evidence, deliberate on contested issues, and issue guidance to policymakers. Such platforms function as trusted arbiters amid conflicting claims and evolving knowledge, helping to balance competing interests and values without succumbing to reductive technocratic impulses.

The strategy advocated by Bommasani and colleagues represents a paradigm shift from reactive, fragmented policymaking toward anticipatory and evidence-rooted governance. By rooting regulations in rigorous, continuously updated scientific understanding, societies can better harness AI’s transformative potential while mitigating its attendant risks. This iterative, evidence-based approach embraces complexity and uncertainty, acknowledging that responsible AI governance is an ongoing, collaborative endeavor requiring sustained commitment across sectors and geographies.

Notably, the article situates these principles within ongoing debates surrounding AI safety, ethics, and public trust. It implicitly critiques sensationalist portrayals of AI—ranging from dystopian fears to uncritical techno-optimism—and underscores the need for measured, empirically grounded discourse. Such balanced framing is essential to mobilize informed civic engagement, promote transparency, and ensure that AI development aligns with broadly shared human values.

In conclusion, the call to action issued by Bommasani et al. challenges policymakers, researchers, and industry leaders alike to embrace a science- and evidence-based framework for AI governance. This involves systematically expanding the evidentiary base through rigorous evaluations, guaranteeing transparency, safeguarding independent inquiry, incorporating socio-technical insights, and institutionalizing expert consensus. Only by adhering to these principles can governance structures keep pace with the rapid evolution of AI technologies, ensuring their deployment maximizes societal benefit while minimizing harm.

Subject of Research: Advancing AI policy through scientific evidence and systematic analysis

Article Title: Advancing science- and evidence-based AI policy

News Publication Date: 31-Jul-2025

Web References: 10.1126/science.adu8449

Keywords: Artificial Intelligence, AI Policy, Evidence-Based Governance, Scientific Understanding, AI Safety, Transparency, Independent Research, Post-Deployment Monitoring, Socio-Technical Systems, Expert Consensus

Zweig’s investigation provides a lucid narrative that exposes how some of the nation’s most trusted experts—including renowned journalists and leading health officials—systematically misread data, succumbed to groupthink, and allowed political and ideological biases to undermine sound evidence-based policymaking. Their decisions resulted in an unprecedented denial of in-person education for healthy children, a phenomenon without parallel in recent times in the United States. Notably, this paralytic response starkly contrasted with policies in many European countries, where schools largely remained open throughout the pandemic, enabling students to continue face-to-face learning without exacerbating viral transmission or mortality rates.

This divergence raises fundamental questions about the role of scientific evidence, risk assessment, and policy judgment during crises. Zweig’s book emphasizes that the American school closures were not predicated upon clear empirical data demonstrating that removing children from classrooms materially decreased COVID-19 transmission or fatalities. Instead, the closures were driven by an overabundance of caution—a precautionary principle applied to a scenario rife with uncertainty, but ultimately causing profound social harm. The disproportionate impact on underprivileged communities only compounded pre-existing educational inequities, creating a cascade of mental health struggles, academic setbacks, elevated dropout rates, and physical health issues such as obesity and abuse.

The mental health ramifications alone underscore the devastating cost. The widespread isolation and disruption in routine exacerbated anxiety and depression among children and adolescents, a trend documented in numerous clinical studies. School environments provide more than academic instruction; they are critical venues for social development, psychological stability, and nutritional support. By denying millions of students access to these pillars of wellbeing, policymakers inadvertently inflicted a secondary health crisis that persists today.

Zweig traces how the narrative supporting school closures gained traction amid media coverage and political discourse saturated with fear and incomplete scientific understanding. His investigative acumen reveals how data was selectively interpreted or ignored, resulting in a distorted public perception of the virus’s risk to children. He critiques the scientific establishment’s failure to adapt recommendations as new evidence emerged, particularly from countries demonstrating the safety and efficacy of maintaining in-person education. This phenomenon lays bare the dangers of institutional inertia and ideological rigidity amplified in times of crisis.

What is especially striking about Zweig’s account is his meticulous documentation of how journalistic integrity was compromised. He discusses his personal role in writing the earliest major American article advocating for school reopenings in May 2020, confronting entrenched skepticism and censorship of dissenting views on social media platforms. His reporting on pediatric COVID-19 hospitalization rates and critical analysis of mask mandates contributed to reframing public dialogue but was met with substantial opposition. This interplay between media narratives and public health policy highlights a complex dynamic where information control can significantly shape—and sometimes distort—societal responses to emergencies.

The broader implications of this book extend beyond the immediate context of the pandemic. Zweig uses the flawed policy choices surrounding school closures as a prism through which we can examine systemic issues—such as how bureaucracies manage risk, how cultural and political dynamics influence scientific discourse, and how societies can be ill-prepared to act prudently during uncertain, high-stakes situations. These lessons resonate across multiple domains, urging a recalibration of crisis management strategies to balance precaution with pragmatism and equity.

David Zweig’s work will likely become a cornerstone in pandemic literature, combining rigorous scientific reportage with compelling storytelling. Its exhaustive research, clarity of argument, and compelling ethical reflection serve as a vital cautionary tale for policymakers, scientists, educators, and the public alike. It challenges readers to reconsider the assumptions underlying emergency interventions and the necessity of safeguarding intellectual pluralism and transparency amid crises.

The book has already garnered endorsements from leading figures such as Nate Silver, Paul Romer, Jeffrey S. Flier, Marty Makary, and Matt Taibbi—each recognizing its penetrating insight and urgent relevance. These praises reinforce the notion that An Abundance of Caution is more than a retrospective; it is an indispensable guide to preventing recurrences of similar public health missteps in the future.

Beyond the book’s critique, Zweig’s investigative journalism has had tangible policy impacts. His exposure of flawed CDC guidelines on masking in outdoor camp settings contributed directly to the agency reversing its policies, demonstrating the power and necessity of independent scrutiny in public health affairs. His interviews and research provide a nuanced understanding of vaccine safety signals, misinformation dynamics, and the complex trade-offs involved in pandemic governance.

In sum, An Abundance of Caution meticulously dismantles the myth that school closures were an unavoidable or necessary measure in combating COVID-19. Instead, it argues persuasively that these decisions were the product of a public health apparatus and political ecosystem deeply flawed in their response—a failure with consequences that will reverberate through a generation of American children. This book implores us to confront uncomfortable truths and cultivate more resilient, evidence-based, and equitable frameworks to navigate the inevitable challenges ahead.

Subject of Research: Pandemic school closures, public health decision-making, COVID-19 impact on education

Article Title: An Abundance of Caution: Unraveling the Catastrophic Decisions Behind America’s Pandemic School Closures

News Publication Date: April 2025

Web References:
https://mitpress.mit.edu/9780262549158/an-abundance-of-caution/

Image Credits: The MIT Press, 2025

Keywords: COVID-19, pandemic, school closures, public health, education policy, viral infections, health disparities, child mental health

The study, published in the prestigious journal Nature Human Behavior, combines efforts from a multitude of research institutions, showcasing a collaborative approach to understanding public sentiment toward science. Tim Weninger, a notable contributor to the research and professor at the University of Notre Dame, emphasized the unprecedented nature of this collaborative endeavor in social sciences. This monumental effort highlighted that, contrary to the prevailing belief in a "crisis of trust" in science, a substantial majority of individuals globally exhibit high levels of confidence in scientists.

Among the key findings, researchers noted that 83% of respondents believe that scientists should take it upon themselves to communicate research findings and complex scientific concepts to the public clearly. This desire for engagement underscores a broader expectation that scientists must advocate for transparency and establish a direct line of communication with society. Furthermore, the study found that over half the respondents, approximately 52%, want scientists to have a more influential role in policymaking processes. This reflects a growing recognition of the importance of science-driven policy decisions that can effectively address pressing societal issues like climate change and public health.

Delving deeper into societal shifts, the survey established a connection between demographics and levels of trust in the scientific community. Factors such as gender, age, educational attainment, income level, and urban versus rural residency played crucial roles in shaping perceptions. Notably, trust was observed to be higher among women, older individuals, urban residents, and those with left-leaning political ideologies. Interestingly, the study’s findings challenge preconceived notions that political orientation would consistently influence trust levels; instead, research indicates that such correlations may not be universally applicable across different cultural contexts.

One particularly surprising outcome of the study was the positive correlation between religiosity and trust in science, suggesting that many individuals can reconcile beliefs in science and religion. This finding contests the stereotype of a deep-seated conflict between scientific and religious perspectives, revealing a nuanced landscape in public perceptions of science. Encouragingly, this aspect of the study showcases opportunities for dialogue and collaboration between the scientific community and religious organizations, aiming to promote a shared understanding of factual evidence, especially in times of widespread misinformation.

As the survey shifts its focus to the public’s aspirations for scientists’ roles, it highlights a significant inclination towards addressing global challenges, such as health crises and environmental sustainability. Participants expressed a desire for scientists to prioritize research that enhances public health, alleviates poverty, and tackles energy challenges rather than focusing predominantly on defense-related technologies. This sentiment indicates a clear public appetite for research directed toward social good rather than military advancements, prompting a reevaluation of research funding priorities.

Building upon these findings, the authors of the study argue for a renewed emphasis on scientists’ communication strategies. Enhanced engagement and dialogue between scientists and various public audiences are essential not only to nurture trust but also to cultivate a deeper understanding of scientific processes and findings. The survey advocates for an inclusive approach to science communication, reaching out to diverse demographic groups while paying particular attention to conservative populations in Western nations—an area that has historically seen a significant trust gap in science.

While trust in scientists remains comparatively high, the survey highlights areas for improvement. A significant percentage, around 42%, of respondents felt that scientists are not sufficiently receptive to feedback or open to public input. This insight serves as a warning sign—suggesting that without attentiveness to public sentiment, the fragile bond of trust could face new challenges. Addressing these concerns requires a proactive approach from the scientific community, acknowledging the necessity of active listening and responsiveness to community needs.

The findings from this comprehensive survey illuminate the current landscape of public trust in science against a backdrop of prior studies that previously suggested declining trust levels, particularly in the United States and Europe. The results challenge this narrative by presenting a more optimistic picture of public sentiment. The research encourages scientists to focus on building and fortifying trust through engaging narratives, transparent communication of research, and active participation in matters that resonate with public values and priorities.

In summary, this expansive investigation into the trustworthiness of scientists reinforces the imperative of engagement, communication, and connection between the scientific community and society at large. By embracing an approach grounded in transparency and responsiveness, scientists can enhance their credibility, promote informed public discourse, and foster a culture of collaboration that transcends traditional boundaries.

The implications of this research extend far beyond academic interest; they underscore the critical role scientists play in navigating global challenges and shaping a future that values evidence-based decision-making. As the world faces ongoing issues of climate change, public health crises, and societal disparities, the survey’s findings compel the scientific community to step forward as champions of informed policy and advocates for the common good.

Ongoing public disillusionment with expertise amid the spread of misinformation may make it imperative for scientists to adopt new approaches that prioritize storytelling and emotional resonance in their communication strategies. Engaging the public not just with data but with narratives that resonate on a personal level will be key in maintaining trust and legitimizing the role of science in shaping societal progress.

Ultimately, this study serves as a clarion call for scientists to engage deeply with the communities they serve, reinforcing the importance of dialogue, collaboration, and shared goals. The commitment to fostering trust and understanding relies on recognizing that science doesn’t exist in a vacuum—it thrives in a vibrant ecosystem of communication, education, and proactive outreach. As the scientific community embraces these findings and adapts accordingly, there is hope that greater public engagement will lead to a future where trust in scientists not only persists but flourishes.

Subject of Research: Trust in Scientists
Article Title: Trust in scientists and their role in society across 68 countries
News Publication Date: 20-Jan-2025
Web References: Nature Human Behavior
References: DOI
Image Credits: Photo by Barbara Johnston/University of Notre Dame

Keywords: Trust in science, scientific communication, public engagement, science policy, global perceptions, demographics, societal roles