Public Confidence in Science

<p>This thesis draws on social constructivist theories of scientific knowledge to analyse the public engagement practices of a cohort of scientist-communicators in Aotearoa as they represent scientific complexity, risk, and uncertainty in public. Through semi-structured interviews and thematic analysis, this thesis demonstrates that participants think defensively about the publics they communicate to, drawing boundaries between science and publics that minimise exposure of the elements of scientific knowledge they perceive might undermine scientific authority. Such boundary-work often demarcates public engagement from scientific knowledge production, constructing public engagement as a subjective process applied to scientific knowledge after the fact. These science-communicators also work to overcome these very same boundaries by making science more accessible and democratic. Such tensions suggest that participants not only socially construct science, but also contribute to the social construction of public engagement with science as they work to transform systemic and cultural barriers acting to entrench science as an inaccessible, exclusive, and unilateral arbiter of knowledge. In doing so, participants found that presenting a more accurate, complex picture of science—with all its uncertainties and failures—had not undermined public confidence in science. Instead, complexity, risk and uncertainty could become transparent elements of scientific knowledge production, thereby open to public scrutiny and definition. Participants’ representations of complexity, risk, and uncertainty were influenced by accessible, local publications, and economic and institutional conditions, but rarely by established public engagement scholarship.</p>

<p>Open science has continued to gain traction for reasons that are both aspirational—building public confidence in science, adding more diverse voices to the research conversation—and practical, such as increasing the pace of discovery, enabling verification, and leveraging emerging technologies such as machine learning. Given global technical and policy developments, the movement is rapidly approaching a moment of truth. This timely coordination of open science approaches across communities and domains will both accelerate the transition and increase the likelihood that these fast-sprouting green shoots grow into a lush and verdant communal garden: well-tended, sustainable, and accessible to all.</p>

Although the United Kingdom (UK) has no explicit ban on genetically modified (GM) crops, by mid-2005 cultivation of GM crops has uncertain prospects. The UK has taken a ‘precautionary approach’ that has engaged policy actors in a participatory process going beyond usual regulatory procedures. It has involved a less formal procedure than that advocated by the European Commission’s guidelines on precaution. Attempts to accommodate diverse perspectives in a policy decision for GM crops have engaged policy actors in a learning process that raised broader questions concerning agriculture more generally. It has highlighted the need for flexible policy measures, particularly at the European level. However, this process may undermine, rather than help to achieve, the stated Government aim, which is to restore public confidence in science as the basis for policy making.

Recent advances in Bayesian hypothesis testing have led to the development of uniformly most powerful Bayesian tests, which represent an objective, default class of Bayesian hypothesis tests that have the same rejection regions as classical significance tests. Based on the correspondence between these two classes of tests, it is possible to equate the size of classical hypothesis tests with evidence thresholds in Bayesian tests, and to equate P values with Bayes factors. An examination of these connections suggest that recent concerns over the lack of reproducibility of scientific studies can be attributed largely to the conduct of significance tests at unjustifiably high levels of significance. To correct this problem, evidence thresholds required for the declaration of a significant finding should be increased to 25-50:1, and to 100-200:1 for the declaration of a highly significant finding. In terms of classical hypothesis tests, these evidence standards mandate the conduct of tests at the 0.005 or 0.001 level of significance.

SECTION In probabilistic disciplines from psychology to cancer biology and behavioral ecology, a disturbing quantity of empirically derived understanding has been challenged and found wanting (Begley and Ellis, 2012; Carpenter, 2012; Parker, 2013). Recently, it was reported that 47 of 53 “landmark” cancer studies from the past decade could not be reproduced (Begley and Ellis, 2012). Ongoing attempts to replicate results in psychology (Carpenter, 2012) have found that substantial portions do not stand subsequent tests (Reproducibility Project: https://osf. io/ezcuj/). Although some well-publicized cases of data fabrication have plagued that field recently (Vogel, 2011), much of the lack of repeatability is expected to result from less nefarious forms of bias (Ioannidis, 2005). Closer to home, a recent meta-analysis of studies of plumage color in a European songbird has substantially clouded what had been hailed as a model for the understanding of plumage color and sexual selection (Parker, 2013). The crux of the problem is that the published literature, especially in highly probabilistic systems, suffers from inflated type I error (false positive) rates, and careful replication is too rare to reliably separate the robust results from those resulting from error (Ioannidis, 2005; Parker, 2013). Thus, many published results are incorrect, and these results are too rarely discredited. Concerns about problems of empirical error are receiving attention from prestigious journals (e.g., Nature; Nuzzo, 2014), and in the popular press (e.g., Lehrer, 2010; Anonymous, 2013) they have stimulated a discourse that may be eroding public confidence in science. Strategies to reduce the problems of inflated error and infrequent replication are emerging in psychology, neuroscience, and medicine (Baker, 2012; Carpenter, 2012). High rates of type I error and low rates of replication may appear to result primarily from the decisions of individual researchers. These researchers are, however, responding to institutional incentive structures. For instance, funding bodies support novel projects to the exclusion of replications, and high impact journals also place a premium on novelty (Palmer, 2000; Kelly, 2006). As another example, most journals select articles based on study outcome rather than just soundness of hypothesis, predictions, and methods (Chambers, 2013). Thus, researchers often choose to report the most interesting subsets of results or pursue other forms of biased reporting rather than reporting the entire set of outcomes (John et al., 2012). Institutions also promote bias, and possibly even academic dishonesty, by basing professional evaluation and remuneration on number of publications and the stature of the journals in which they are published (Qiu, 2010; John et al., 2012). Thus, effective strategies will come from changes in the institutions that influence our research practices, such as professional societies (including journals) and funding agencies (Parker, 2013). It is precisely at this institutional level that psychology and medicine are tackling the challenge of reducing bias and increasing replication. Initiatives in these other disciplines are not necessarily templates directly transferable to ecology and evolution. Yet, such examples should serve to stimulate discussion and they clearly demonstrate that redesigning incentive structures is possible. Reducing incomplete and biased reporting of results may be accomplished by encouraging or requiring registration of studies at their initiation (Schooler, 2011). Since 2000, the US government has provided a registry for clinical trials of medical interventions (ClinicalTrials.gov). Registration prior to initiation is a requirement of many funding agencies and medical journals, and thus has become “standard practice” (Huser and Cimino, 2013). Although results from approximately half of registered trials end up unpublished, about a third of the unpublished studies post some results in the registry (Ross et al., 2009). Further, the registry facilitates a more precise estimate of reporting bias, and provides contact information for researchers with unpublished work. Thus, the bias in available results has dropped along with our ignorance of this bias. These are highly desirable outcomes. A conceptually similar idea is the “registered report” initiated by the neuroscience journal Cortex in 2013 (Chambers, 2013). To publish in the registered report section of the journal, researchers submit a study plan for peer review and conditional acceptance prior to gathering data (http://www.elsevier.com/journals/cortex/ 0010-9452/guide-for-authors). This counteracts several forms of publication bias, including editors’ preferences for statistically significant or novel outcomes, and

Research misconduct is harmful because it threatens public health and public safety, and also undermines public confidence in science. Efforts to eradicate ongoing and prevent future misconduct are numerous and varied, yet the question of “what works” remains largely unanswered. To shed light on this issue, this study used data from both mail and online surveys administered to a stratified random sample of tenured and tenure-track faculty members (N = 613) in the social, natural, and applied sciences at America’s top 100 research universities. Participants were asked to gauge the effectiveness of various intervention strategies: formal sanctions (professional and legal), informal sanctions (peers), prevention efforts (ethics and professional training), and reducing the pressures associated with working in research-intensive units. Results indicated that (1) formal sanctions received the highest level of support, (2) female scholars and researchers working in the applied sciences favored formal sanctions, and (3) a nontrivial portion of the sample supported an integrated approach that combined elements of different strategies. A key takeaway for university administrators is that a multifaceted approach to dealing with the problem of research misconduct, which prominently features enhanced formal sanctions, will be met with the support of university faculty.

What are the implications of our results for scientific and religious communities? Drawing on the core empirical patterns discussed in the book, this chapter explains how the rhetoric of New Atheism espoused by celebrity scientists does not square with the reality of atheism experienced by atheist scientists in the U.S. and U.K. Religious communities may not be aware of atheist scientists’ actual views of faith, scientism, their moral orientations, or—for atheists who grew up religious—the reasons why they abandoned past faith. And, there is more common ground between the scientific and religious communities than either group is aware, offering fertile ground for dialogue. A diverse array of stakeholders should be involved in such pursuits, including national organizations, universities, churches, and science communicators. The success of these efforts have implications for public confidence in science and diversity within the profession.