The controversy over genetically modified [GM] organisms is often framed in terms of possible hazards for human health. Articles in a previous volume of this *Encyclopedia* give a general overview of GM crops [@Mulvaney2014] and specifically examine human health [@Nordgard2014] and labeling [@Bruton2014] issues surrounding GM organisms. This article explores several other aspects of the controversy: environmental concerns, political and legal disputes, and the aim of "feeding the world" and promoting food security. Rather than discussing abstract, hypothetical GM organisms, this (...) article explores the consequences of the GM organisms that have actually been deployed in the particular contexts that they have been deployed, on the belief that there is little point in discussing GM organisms in an idealized or context-independent way. (shrink)

It is commonly argued that values “fill the logical gap” of underdetermination of theory by evidence, namely, values affect our choice between two or more theories that fit the same evidence. The underdetermination model, however, does not exhaust the roles values play in evidential reasoning. I introduce WAVE – a novel account of the logical relations between values and evidence. WAVE states that values influence evidential reasoning by adjusting evidential weights. I argue that the weight-adjusting role of values is distinct (...) from their underdetermination gap-filling role. Values adjust weights in three ways. First, values affect our trust in the testimony of others. Second, values influence the evidential thresholds required for justified epistemic judgments. Third, values influence the relative weight of a certain type of evidence within a body of multimodal discordant evidence. WAVE explains, from an epistemic perspective, rather than psychological, how smokers, for example, can find the same evidence about the dangers of smoking less persuasive than non-smokers. WAVE allows for a wider effect of values on our accepted scientific theories and beliefs than the effect for which the underdetermination model allows alone; therefore, science studies scholars must consider WAVE in their research and analysis of evidential case studies. (shrink)

In recent papers and a book, Heather Douglas has expanded on the well-known argument from inductive risk, thereby launching an influential contemporary critique of the value-free ideal for science. This paper distills Douglas’s critique into four major claims. The first three claims provide a significant challenge to the value-free ideal for science. However, the fourth claim, which delineates her positive proposal to regulate values in science by distinguishing direct and indirect roles for values, is ambiguous between two interpretations, and both (...) have weaknesses. Fortunately, two elements of Douglas’s work that have previously received much less emphasis provide resources for developing a more promising approach for regulating values in science. (shrink)

Scientific consensus is widely deferred to in public debates as a social indicator of the existence of knowledge. However, it is far from clear that such deference to consensus is always justified. The existence of agreement in a community of researchers is a contingent fact, and researchers may reach a consensus for all kinds of reasons, such as fighting a common foe or sharing a common bias. Scientific consensus, by itself, does not necessarily indicate the existence of shared knowledge among (...) the members of the consensus community. I address the question of under what conditions it is likely that a consensus is in fact knowledge based. I argue that a consensus is likely to be knowledge based when knowledge is the best explanation of the consensus, and I identify three conditions—social calibration, apparent consilience of evidence, and social diversity, for knowledge being the best explanation of a consensus. (shrink)

. The potential for financial conflicts of interest to influence scientific research has become a significant concern. Some commentators have suggested that the development of standardized study protocols could help to alleviate these problems. This paper identifies two problems with this solution: scientific research incorporates numerous methodological judgments that cannot be constrained by standardized protocols; and standardization can hide significant value judgments. These problems arise because of four weaknesses of standardized guidelines: incompleteness, limited applicability, selective ignorance, and ossification. Therefore, the (...) standardization of study designs should not serve as an alternative to addressing the interests and power relations that pervade science policy-making. Policy makers should take at least two steps to prevent powerful interest groups from co-opting standardized guidelines. First, their development and review should be made as transparent as possible and should be subjected to broadly-based deliberation. Second, standardized guidelines should be supplemented with efforts to scrutinize the conditions under which financial COIs tend to have the most worrisome effects so that additional steps can be taken to eliminate and mitigate those conditions. (shrink)

This paper examines the scientific controversy over the yields of genetically modified [GM] crops as a case study in epistemologically deep disagreements. Appeals to “the evidence” are inadequate to resolve such disagreements; not because the interlocutors have radically different metaphysical views (as in cases of incommensurability), but instead because they assume rival epistemological frameworks and so have incompatible views about what kinds of research methods and claims count as evidence. Specifically, I show that, in the yield debate, proponents and opponents (...) of GM crops cite two different sets of claims as evidence, which correspond to two rival epistemological frameworks, classical experimental epistemology and Nancy Cartwright's evidence for use. I go on to argue that, even if both sides of the debate accepted Cartwright's view, they might still disagree over what counts as evidence, because evidence for use ties standards of evidence to what is sometimes called the “context of application.”. (shrink)

Evidence hierarchies are widely used to assess evidence in systematic reviews of medical studies. I give several arguments against the use of evidence hierarchies. The problems with evidence hierarchies are numerous, and include methodological shortcomings, philosophical problems, and formal constraints. I argue that medical science should not employ evidence hierarchies, including even the latest and most-sophisticated of such hierarchies.

A consensus in a scientific community is often used as a resource for making informed public-policy decisions and deciding between rival expert testimonies in legal trials. This paper contains a social-epistemic analysis of the high-profile Bendectin drug controversy, which was decided in the courtroom inter alia by deference to a scientific consensus about the safety of Bendectin. Drawing on my previously developed account of knowledge-based consensus, I argue that the consensus in this case was not knowledge based, hence courts’ deference (...) to it was not epistemically justified. I draw sceptical lessons from this analysis regarding the value of scientific consensus as a desirable and reliable means of resolving scientific controversies in public life. (shrink)

Prediction markets, which trade contracts based on the results of predictions, have been remarkably successful in predicting the results of political events. A number of proposals have been made to extend prediction markets to scientific questions, and some small-scale science prediction markets have been implemented. Advocates for science prediction markets argue that they could alleviate problems in science such as bias in peer review and epistemically unjustified consensus. I argue that bias in peer review and epistemically unjustified consensuses are genuine (...) problems in science, and that current attempts to solve them fail in practical circumstances. Prediction markets do show some promise for answering scientific questions while avoiding the pitfalls of consensus and peer review. However, there are strong reasons to believe that science prediction markets will not perform nearly so well as election prediction markets, and implementing prediction markets on a large scale could have significant unintended consequences for the organization of scientific research. The promise of prediction markets to solve current problems in assessing scientific claims is therefore largely illusory. (shrink)

Given the complexity of most phenomena, we have to delegate much epistemic work to other knowers and we must find reasons for relying on these specific knowers and not others. In our societies, these other knowers are often called experts and we rely on their epistemic authority more and more. For many complex phenomena such as climate change, genetically modified crops, and immunization, the experts that are called upon are scientific experts. For that reason, finding good reasons and justification for (...) deferring to their knowledge is of paramount societal importance. As we shall see, many have analyzed this epistemic dependence on scientific experts and have tried to show in what contexts are there good... (shrink)

Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an environment is to rely on evidence. Despite the recent increase in post-fact figures in politics, evidence-based policymaking takes centre stage in policy-setting institutions. Often, however, policy-makers face large volumes of evidence from different sources. Robustness analysis can, prima facie, handle this evidential diversity. Roughly, a hypothesis is supported by robust evidence if the different evidential sources are in agreement. In this thesis, I strengthen the (...) case for the use of robustness analysis in evidence-based policymaking by answering open research questions about this inference technique. First, I argue that existing taxonomies miss a fruitful category of robustness reasoning, that is predictive stability. Second, I claim that derivational robustness analysis – the investigation of whether the results of different models are in agreement – can yield interesting insights even if not the entire relevant model space is covered by available models or if the model results are only partially in agreement. Third, I claim that expert knowledge is necessary to address questions that arise when one applies measurement robustness analysis – the investigation into whether multiple means of measurement yield the same result. Finally, I argue that, in situations where evidence from different measurements is not in agreement, it can be advisable to no longer take all of the evidence into account. This can be done in a rationally defensible way by choosing the most adequate theory or model underlying parts of the evidence set. I discuss examples from climate, medical, and economic policy-making to establish my claims. (shrink)