Chapters and Abstracts
1 What Makes It Science, and Who Are the Scientists?
What do we mean by "science," and why does it carry such authority in public debates and policy decisions? This chapter explores the evolving concept of science, particularly Western Science, and its privileged role in informing public policy. Beginning with the historical roots and diverse global interpretations of scientific knowledge, we explore how modern science emerged with institutional frameworks, standards of evidence, and communities of practice. Central to this narrative are the Mertonian ideals—Communalism, Universalism, Disinterestedness, and Organized Scepticism—which sustain the credibility and social role of scientists. The standards of proof vary across disciplines. Artificial intelligence and big data are altering the scientific method, and the challenges of communicating scientific uncertainty in policy contexts. The sociology of science is traced through the lenses of Popper's falsifiability, Kuhn’s paradigm shifts, and Latour’s Actor Network Theory. Ultimately, the chapter highlights the dual identity of scientists as both experts and citizens, and the ethical tensions they navigate when engaging in public discourse and policy-making.
2 A Selective History of Western Science
A selective history of Western science traces the development of its institutions, methods, and values from the early Scientific Revolution to the present. This highlights how the scientific method—particularly the shift from deductive to inductive reasoning, as championed by Francis Bacon—became foundational for generating reliable, testable knowledge. Through case studies like Archimedes and the establishment of scientific societies, we learn how science gained autonomy from religious and political authorities, while forging ties with the state through military, economic, and technological utility. The modern “scientist” emerged as a distinct profession through the rise of peer-reviewed communities that govern what counts as legitimate knowledge. Universities became critical centers of research and knowledge production, increasingly aligned with state and industrial interests. The philosophical and institutional challenges that have shaped the authority and accountability of science, set the stage for the evolving relationship between science, society, and policy in the modern era.
3 Observe the Community of Scientists in Action
What are the norms, behaviours, and institutions that allow scientists to maintain credibility, correct errors, and engage with society as trustworthy experts? This question introduces the concept of the epistemic community—a network of disciplinary peers who share standards for evidence, ethics, and scientific practice—and explains how this community governs itself through peer review, codes of conduct, and informal norms. Case studies—from the hydroxychloroquine for Covid-19 controversy to human cloning fraud—illustrate both the strengths and limitations of these self-regulatory systems. “Big Science,” shaped by powerful institutional and commercial forces, is now eroding the ideals of communalism, universalism, disinterestedness, and organized skepticism (CUDOS). While peer review remains a cornerstone of epistemic integrity, it is not always sufficient to address misconduct, especially when science operates outside traditional academic structures. As scientific influence grows in policy, commerce, and global crises, the challenge of preserving epistemic autonomy becomes more urgent—raising questions about transparency, accountability, and trust in modern science.
4 Certainty and Objectivity in Science and Science for Policy
How does science manage uncertainty? It is an inherent and necessary feature of knowledge production—especially when science informs policy. Drawing on examples from climate science, medical research, and public health, the chapter distinguishes between different sources of uncertainty, including measurement error, model assumptions, statistical inference, and epistemic bias. It revisits the philosophical foundations of scientific uncertainty, from Popperian falsifiability to Bayesian inference, and emphasizes how scientific norms—controls, replication, peer review—aim to reduce error while remaining open to revision. The reproducibility crisis is introduced, which highlights how misuse or misunderstanding of statistics (e.g. p-hacking, conflating correlation with causation) can undermine trust in scientific findings. Increasing reliance on complex models and machine learning introduces new challenges in transparency and validation, especially when these tools are used to guide policy under conditions of high stakes and incomplete information. The precautionary principle and the concept of post-normal science are introduced as frameworks for navigating risk in policy decisions. Ultimately, the chapter shows that while science embraces uncertainty as part of discovery, policy prefers certainty—creating persistent tensions at the interface of science and power.
5 Institutions for Science and Institutions for Policy-1452 to 2011
We explore the evolving relationship between science and government through the lens of institutional development, the social contract for science, and science-for-policy mechanisms. Beginning with the founding of early scientific societies, liberal democratic ideals enabled intellectual autonomy in exchange for societal benefits such as public health and technological innovation. The Linear Model of Innovation became the foundational myth sustaining public investment in basic science and explains its role in shaping post-WWII “Big Science” initiatives, such as the Manhattan Project and the Human Genome Project. Through historical case studies—from vaccine development to the Fukushima nuclear disaster and the Thalidomide crisis—we critically examine how science informs policy, succumbs to the risks of regulatory capture and the tensions between objectivity and political influence. Concepts such as the Iron Triangle, boundary work, and philanthrocapitalism are used to show how scientists, policymakers, and stakeholders co-produce knowledge and shape evidence-based policy. The chapter concludes by highlighting emerging challenges in maintaining scientific integrity amid growing commercialisation, stakeholder activism, and populist pressures in democratic systems.
Scientists occupy conflicting roles as stakeholders in the policy-making process. While scientific credibility is traditionally grounded in the CUDOS norms—communalism, universalism, disinterestedness, and organized skepticism—policy impact frequently requires scientists to step beyond these ideals and engage with political, commercial, or advocacy interests. Through case studies such as the public debate over gas stove safety and the rise and fall of Liberation Therapy for multiple sclerosis, we explore how scientists are drawn into stakeholder positions, often motivated by a desire to inform or influence policy. To structure the analysis, Roger Pielke’s framework of scientist roles is introduced—comprised of the Pure Scientist, Applied Scientist, Issue Advocate, Science Arbiter, Honest Broker, and Science Advisor (to which is added the Policy Wonk). We interpret how each role navigates between preserving epistemic integrity and meeting regulatory or political expectations. Scientists face a Catch-22: the more they adhere to objective communication, the less persuasive they are to policymakers and the public. In contrast, advocacy compromises the impartiality that underpins their authority. The Catch-22 is magnified in high-stakes, value-laden debates where urgency demands action before consensus is reached. Boundary organizations such as Europe’s Scientific Advice Mechanism (SAM) attempt to structure and insulate science input from overt political influence, while still rendering it "policy-ready." Ultimately, scientists cannot remain apolitical observers in contentious policy arenas—they must choose how to engage, knowing that both action and inaction have consequences for public trust and policy outcomes.
7 Emergence of Post Normal Science
Post Normal Science (PNS) is a way of understanding why conventional approaches to science advice often falter in today’s high-stakes policy debates. In contexts where urgency is high, facts are incomplete, and societal values clash—as with climate change, gene editing, or pandemic responses—scientific input alone rarely settles the issue. Instead, scientists find themselves operating in a noisy and contested information and policy environment, where their authority is diluted by competing stakeholders, media distortion, and politicised narratives. The chapter moves beyond the limitations of the Linear Model to show how uncertainty, advocacy, and institutional inertia complicate evidence-based decision-making.
Through examples ranging from the banning of Alar to the Montreal Protocol and Liberation Therapy, we explore how science can become entangled with manufactured urgency, misrepresented risk, and stakeholder influence. It categorises common dysfunctions of science-for-policy—such as scientised politics, noble lies, and "mad rationality"—and considers how tools like the precautionary principle and risk management are deployed, sometimes selectively, in these contexts. The growing role of social media and celebrity activism further blurs the boundary between scientific communication and persuasion. Rather than offering a technical fix, PNS provides a framework to diagnose these dynamics and invites new models of deliberation that reflect the realities of uncertainty, pluralism, and public engagement.
8 Science for Policy Lessons from Authoritarian States
How does science-for-policy operate within authoritarian states in contrast to governments with the liberal-democratic ideals of the West? Authoritarian regimes often value scientific input—particularly in engineering, military, and economic development—but typically constrain the autonomy and openness that define scientific communities in democratic societies. The chapter focuses on the Soviet Union and the People’s Republic of China to show how science can flourish when aligned with state priorities, yet becomes vulnerable when ideological conformity, repression, or utilitarian objectives override epistemic norms.
Case studies—including the politicisation of genetics under Stalin, environmental activism at Lake Baikal, and China's contested Nu River hydroelectric project—reveal how science must navigate power structures that limit public dissent and restrict access to decision-making. These examples highlight the mechanisms authoritarian governments use to filter, co-opt, or suppress scientific advice, while also illustrating how scientists, NGOs, and even artists can occasionally shape policy through indirect influence or strategic alignment with state goals. The chapter also reflects on the recent drift toward populism in some democratic nations, noting similar tactics of restricting academic freedom, politicising expertise, and undermining institutional independence. As science becomes increasingly entangled with power with illiberalism and populism, understanding how it is mobilised—or marginalised—offers insight into both its potetial and its limits in shaping public policy under constrained political conditions.
9 Where is Science for Policy Going?
How is the relationship between science and policy changing in an era marked by technological disruption, media fragmentation, and political polarization? The traditional perception of science advising—where expert knowledge is delivered to policymakers in a linear, depoliticized fashion—is increasingly unfit for addressing today’s “post-normal” policy challenges, defined by high stakes, contested values, and pervasive uncertainty. To improve policy relevance and societal trust, science must be made more usable and socially robust. This entails co-production of knowledge with extended peer communities that include not only experts, but also stakeholders and lay citizens. The chapter surveys methods emerging from Post Normal Science (PNS), Adaptive Management, and Robust Decision Making that aim to incorporate diverse perspectives, values, and types of knowledge. Case studies highlight how involving non-traditional actors in scientific assessment can increase legitimacy and local uptake of policies. Local activities like citizen juries and community science are contrasted with global efforts, such as the IPCC and IPBES’s contrasting approaches to global consensus. All approaches face major challenges of scalability and timeliness. While local, face-to-face engagements often succeed in building trust and integrating multiple perspectives, these successes are difficult to replicate at national or global levels, especially when rapid coordination is required.
Ultimately, the author argues that advancing science-for-policy in the 21st century requires both institutional reform and cultural change. Scientists must move beyond the role of detached advisors and embrace more participatory, deliberative, and transparent forms of engagement. This includes recognising the value of local and experiential knowledge, developing tools to communicate uncertainty effectively, and rethinking how science can contribute to decision-making without demanding epistemic supremacy. In a world where AI may shape public perception as much as scientific consensus, and where populist movements undermine institutional legitimacy, building socially robust knowledge through extended communities may offer the best hope for restoring trust and navigating complexity—though the road ahead demands patience, flexibility, and humility.
10 Applying a Case Study Approach to Science for Policy
This chapter presents a structured case study approach to analysing how science interacts with policy-making, particularly under the challenging conditions of uncertainty, high stakes, and conflicting values—key features of “post-normal science” (PNS). The case studies are designed to be used as a classroom and individual learning tool and for those following emerging science for policy controversies. The case study framework considers elements such as scientific uncertainty, stakeholder interests, epistemic community dynamics, and political context. Students can analyse how decisions are influenced not just by evidence, but by who produces it, how it is communicated, and who controls the policy narrative.
The infamous British Bovine Spongiform Encephalopathy (BSE) crisis is used as an example case, to illustrate how epistemic differences between scientific communities, political pressures, and poor risk communication can delay action and erode public trust. The BSE case is dissected into components—narrative, risk context, scientific background, stakeholder roles, uncertainties, and political dynamics—to detect and interpret systemic failures in risk management and precautionary action.
To aid reproducibility and comparison, the chapter introduces a “post-normal catalogue” tool for mapping how features such as CUDOS violations, exclusion of lay voices, and politicisation of science influence outcomes. This analytic model empowers readers to interpret and compare cases where science meets policy, to facilitate deeper understanding and generalization across cases, enabling readers to navigate the complexities of science-policy interactions more effectively. The chapter includes another 9 complete or incomplete case studies using the new framework. Incomplete studies and the PNS checklist can be used in the classroom as active learning tools.
