The role of values in scientific research has become an important topic of discussion in both scholarly and popular debates. Pundits across the political spectrum worry that research on topics like climate change, evolutionary theory, vaccine safety, and genetically modified foods has become overly politicized. At the same time, it is clear that values play an important role in science by limiting unethical forms of research and by deciding what areas of research have the greatest relevance for society. Deciding how (...) to distinguish legitimate and illegitimate influences of values in scientific research is a matter of vital importance.Recently, philosophers of science have written a great deal on this topic, but most of their work has been directed toward a scholarly audience. This book makes the contemporary philosophical literature on science and values accessible to a wide readership. It examines case studies from a variety of research areas, including climate science, anthropology, chemical risk assessment, ecology, neurobiology, biomedical research, and agriculture. These cases show that values have necessary roles to play in identifying research topics, choosing research questions, determining the aims of inquiry, responding to uncertainty, and deciding how to communicate information. Kevin Elliott focuses not just on describing roles for values but also on determining when their influences are actually appropriate. He emphasizes several conditions for incorporating values in a legitimate fashion, and highlights multiple strategies for fostering engagement between stakeholders so that value influences can be subjected to careful and critical scrutiny. (shrink)

Exploring the role of values in scientific inquiry, Hugh Lacey examines the nature and meaning of values, and looks at challenges to the view, posed by postmodernists, feminists, radical ecologists, Third-World advocates and religious fundamentalists, that science is value free. He also focuses on discussions of 'development', especially in Third World countries. This paperback edition includes a new preface.

"The pursuit of science by professional scientists every day bears less and less resemblance to the perception of science by the general public. It is not the rule-based, methodical system for accumulating facts that dominates the public view. Rather it is the idiosyncratic, often bumbling search for understanding in mostly uncharted places. It is full of wrong turns, cul-de-sacs, mistaken identities, false findings, errors of fact and judgment-and the occasional remarkable success. The widespread but distorted view of science as infallible (...) originates in an education system that teaches nothing but facts using very large, very frightening textbooks, and is spread by media that report on discoveries but almost never on process. It is further reinforced by politicians who pay for it and want to use it to determine policy and therefore want it right and, worst of all, sometimes by scientists who learn early on that talking too much about failures and not enough about successes can harm their careers. Failure, then, is a book that seeks to make science more appealing by exposing its faults. In this sequel to Ignorance, Stuart Firestein shows us that scientific enterprise is riddled with failures, and that this is not only necessary but good. Failure reveals how science got its start, when humans began to use a process-trial and error-as a kind of recipe that includes a hefty dose of failure. It gives the non-scientifically trained public an insider's view of how science is actually done, with the aim of making it accessible, comprehensible, and entertaining."--Publisher description. (shrink)

In recent years, the value-freeness of science has come under extensive critique. Early objectors to the notion of value-free science can be found in Rudner and Churchman, later objections occur in Leach and Gaa, and more recent critics are Kitcher, Douglas, and Elliott. The goal of this paper is to examine and critique two arguments opposed to the notion of a value-free science. The first argument, the uncertainty argument, cites the endemic uncertainty of science and concludes that values are needed (...) to give direction to scientific investigation. The second, or moral argument, cites the fact that scientists have moral obligations just like everyone else, and.. (shrink)

Underdetermination arguments support the conclusion that no amount of empirical data can uniquely determine theory choice. The full content of a theory outreaches those elements of it (the observational elements) that can be shown to be true (or in agreement with actual observations).2 A number of strategies have been developed to minimize the threat such arguments pose to our aspirations to scientific knowledge. I want to focus on one such strategy: the invocation of additional criteria drawn from a pool of (...) cognitive or theoretical values, such as simplicity or generality, to bolster judgements about the worth of models, theories, and hypotheses. What is the status of such criteria? Larry Laudan, in Science and Values, argued that cognitive values could not be treated as self-validating, beyond justification, but are embedded in a three-way reticulational system containing theories, methods, and aims or values, which are involved in mutually supportive relationships (Laudan, 1984). My interest in this paper is not the purportedly self-validating nature of cognitive values, but their cognitive nature. Although Laudan rejects the idea that what he calls cognitive values are exempt from rational criticism and disagreement, he does seem to think that the reticulational system he identifies is independent of non-cognitive considerations. It is this cognitive/non-cognitive distinction that I wish to query in this paper. Let me begin by summarizing those of my own views about inquiry in which this worry about the distinction arises. (shrink)

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

The debate about the rational and the social in science has sometimes been developed in the context of a distinction between epistemic and non-epistemic values. Paying particular attention to two important discussion in the last decade, by Longino and by McMullin, I argue that a fuller understanding of values in science ultimately requires abandoning the distinction itself. This is argued directly in terms of an analysis of the lack of clarity concerning what epistemic values are. I also argue that the (...) philosophical import of much of the feminist work in philosophy of science is restricted by any kind of strict adherence to the distinction. (shrink)

A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a "crisis of reproducibility" to the rise of fraudulent, careless and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out that reproducibility is not only interpreted in different ways, but also serves a variety of epistemic functions depending on the research at hand. Given such variation, I argue that the uncritical pursuit (...) of reproducibility as an overarching epistemic value is misleading and potentially damaging to scientific advancement. Requirements for reproducibility, however they are interpreted, are one of many available means to secure reliable research outcomes. Furthermore, there are cases where the focus on enhancing reproducibility turns out not to foster high-quality research. Scientific communities and Open Science advocates should learn from inferential reasoning from irreproducible data, and promote incentives for all researchers to explicitly and publicly discuss their methodological commitments, the ways in which they learn from mistakes and problems in everyday practice, and the strategies they use to choose which research component of any project needs to be preserved in the long term, and how. (shrink)

Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.

Current fears of a “reproducibility crisis” have led researchers, sources of scientific funding, and the public to question both the efficacy and trustworthiness of science. Suggested policy changes have been focused on statistical problems, such as p-hacking, and issues of experimental design and execution. However, “reproducibility” is a broad concept that includes a number of issues. Furthermore, reproducibility failures occur even in fields such as mathematics or computer science that do not have statistical problems or issues with experimental design. Most (...) importantly, these proposed policy changes ignore a core feature of the process of scientific inquiry that occurs after reproducibility failures: the integration of conflicting observations and ideas into a coherent theory. (shrink)