In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...) criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities. (shrink)

Increasingly, epistemologists are becoming interested in social structures and their effect on epistemic enterprises, but little attention has been paid to the proper distribution of experimental results among scientists. This paper will analyze a model first suggested by two economists, which nicely captures one type of learning situation faced by scientists. The results of a computer simulation study of this model provide two interesting conclusions. First, in some contexts, a community of scientists is, as a whole, more reliable when its (...) members are less aware of their colleagues' experimental results. Second, there is a robust tradeoff between the reliability of a community and the speed with which it reaches a correct conclusion. ‡The author would like to thank Brian Skyrms, Kyle Stanford, Jeffrey Barrett, Bruce Glymour, and the participants in the Social Dynamics Seminar at University of California–Irvine for their helpful comments. Generous financial support was provided by the School of Social Science and Institute for Mathematical Behavioral Sciences at UCI. †To contact the author, please write to: Department of Philosophy, Baker Hall 135, Carnegie Mellon University, Pittsburgh, PA 15213-3890; e-mail: [email protected]. (shrink)

"Why should we care about having true beliefs? And why do demonstrably false beliefs persist and spread despite consequences for the people who hold them? Philosophers of science Cailin O’Connor and James Weatherall argue that social factors, rather than individual psychology, are what’s essential to understanding the spread and persistence of false belief. It might seem that there’s an obvious reason that true beliefs matter: false beliefs will hurt you. But if that’s right, then why is it irrelevant to many (...) people whether they believe true things or not? In an age riven by "fake news," "alternative facts," and disputes over the validity of everything from climate change to the size of inauguration crowds, the authors argue that social factors, not individual psychology, are what’s essential to understanding the persistence of false belief and that we must know how those social forces work in order to fight misinformation effectively."–Publisher’s description. (shrink)

In their recent book, Oreskes and Conway describe the ‘tobacco strategy’, which was used by the tobacco industry to influence policymakers regarding the health risks of tobacco products. The strategy involved two parts, consisting of promoting and sharing independent research supporting the industry’s preferred position and funding additional research, but selectively publishing the results. We introduce a model of the tobacco strategy, and use it to argue that both prongs of the strategy can be extremely effective—even when policymakers rationally update (...) on all evidence available to them. As we elaborate, this model helps illustrate the conditions under which the tobacco strategy is particularly successful. In addition, we show how journalists engaged in ‘fair’ reporting can inadvertently mimic the effects of industry on public belief. 1Introduction2Epistemic Network Models3Selective Sharing4Biased Production5Journalists as Unwitting Propagandists6ConclusionAppendix. (shrink)

Using a decision theoretic model of scientists’ time allocation between potential research projects I explain the fact that on average women scientists publish less research papers than men scientists. If scientists are incentivised to publish as many papers as possible, then it is necessary and sufficient for a productivity gap to arise that women scientists anticipate harsher treatment of their manuscripts than men scientists anticipate for their manuscripts. I present evidence that women do expect harsher treatment and that scientists’ are (...) incentivised to publish as many papers as possible, and discuss some epistemological consequences of this conjecture. (shrink)

Recent philosophical work has praised the reward structure of science, while recent empirical work has shown that many scientific results may not be reproducible. I argue that the reward structure of science incentivizes scientists to focus on speed and impact at the expense of the reproducibility of their work, thus contributing to the so-called reproducibility crisis. I use a rational choice model to identify a set of sufficient conditions for this problem to arise, and I argue that these conditions plausibly (...) apply to a wide range of research situations. Currently proposed solutions will not fully address this problem. Philosophical commentators should temper their optimism about the reward structure of science. (shrink)

Media attention to retracted research suggests that a substantial number of papers are corrupted by misinformation. In reality, every paper contains misinformation; at issue is whether the balance of correct versus incorrect information is acceptable. This paper postulates that analysis of retracted research papers can provide insight into medical misinformation, although retracted papers are not a random sample of incorrect papers. Error is the most common reason for retraction and error may be the principal cause of misinformation as well. Still, (...) one-quarter of retracted papers are fraudulent, and misinformation may also arise through fraud. This paper hypothesises that error and fraud are the main sources of misinformation and that error is more common than fraud. Retraction removes misinformation from the literature; bias is non-retracted misinformation. Bias arises when scientific impropriety results in false research findings. Impropriety can involve experimental design, data collection, data analysis, or data presentation. Yet impropriety also arises through earnest error or statistical naiveté; not all bias is fraud. Several measures are proposed to minimise misinformation in the medical literature, including: greater detail in the clinical trial registry, with rigorous definition of inclusion and exclusion criteria and primary endpoints; clear statistical criteria for every aspect of clinical trials, especially sample size; responsibility for data integrity that accrues to all named authors; increased transparency as to how the costs of research were paid; and greater clarity as to the reasons for retraction. Misinformation can arise without malicious intent; authors of incorrect papers are owed a presumption of incompetence, not malice. (shrink)

IMITATION is a matter of such familiarity to us all that it goes usually unattended to: so much so that professed psychologists have left it largely undiscussed. Whether it be one of the more ultimate facts or not, suppose we assume it to be so; let us then see what we can explain by it, and where we may be able to trace its influence in the developed mind.

Next SectionBackground Scientific papers are retracted for many reasons including fraud (data fabrication or falsification) or error (plagiarism, scientific mistake, ethical problems). Growing attention to fraud in the lay press suggests that the incidence of fraud is increasing. Methods The reasons for retracting 742 English language research papers retracted from the PubMed database between 2000 and 2010 were evaluated. Reasons for retraction were initially dichotomised as fraud or error and then analysed to determine specific reasons for retraction. Results Error was (...) more common than fraud (73.5% of papers were retracted for error (or an undisclosed reason) vs 26.6% retracted for fraud). Eight reasons for retraction were identified; the most common reason was scientific mistake in 234 papers (31.5%), but 134 papers (18.1%) were retracted for ambiguous reasons. Fabrication (including data plagiarism) was more common than text plagiarism. Total papers retracted per year have increased sharply over the decade (r=0.96; p<0.001), as have retractions specifically for fraud (r=0.89; p<0.001). Journals now reach farther back in time to retract, both for fraud (r=0.87; p<0.001) and for scientific mistakes (r=0.95; p<0.001). Journals often fail to alert the naïve reader; 31.8% of retracted papers were not noted as retracted in any way. Conclusions Levels of misconduct appear to be higher than in the past. This may reflect either a real increase in the incidence of fraud or a greater effort on the part of journals to police the literature. However, research bias is rarely cited as a reason for retraction. (shrink)

Industry is a major source of funding for scientific research. There is also a growing concern for how it corrupts researchers faced with conflicts of interest. As such, the debate has focused on whether researchers have maintained their integrity. In this article we draw on both the history of medicine and formal modeling to argue that given methodological diversity and a merit-based system, industry funding can bias a community without corrupting any particular individual. We close by considering a policy solution (...) that may seem to promote unbiased inquiry but that actually exacerbates the problem without additional restrictions. (shrink)

In recent years the social nature of scientific inquiry has generated considerable interest. We examine the effect of an epistemically impure agent on a community of honest truth seekers. Extending a formal model of network epistemology pioneered by Zollman, we conclude that an intransigently biased agent prevents the community from ever converging to the truth. We explore two solutions to this problem, including a novel procedure for endogenous network formation in which agents choose whom to trust. We contend that our (...) model nicely captures aspects of current problems in medical research and gesture at some morals for medical epistemology more generally. (shrink)

Article retraction in research is rising, yet retracted articles continue to be cited at a disturbing rate. This paper presents an analysis of recent retraction patterns, with a unique emphasis on the role author self-cites play, to assist the scientific community in creating counter-strategies. This was accomplished by examining the following: A categorization of retracted articles more complete than previously published work. The relationship between citation counts and after-retraction self-cites from the authors of the work, and the distribution of self-cites (...) across our retraction categories. The distribution of retractions written by both the author and the editor across our retraction categories. The trends for seven of our nine defined retraction categories over a 6-year period. The average journal impact factor by category, and the relationship between impact factor, author self-cites, and overall citations. Our findings indicate new reasons for retractions have emerged in recent years, and more editors are penning retractions. The rates of increase for retraction varies by category, and there is statistically significant difference of average impact factor between many categories. 18 % of authors self-cite retracted work post retraction with only 10 % of those authors also citing the retraction notice. Further, there is a positive correlation between self-cites and after retraction citations. (shrink)

We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...) have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high‐dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small‐world statistics and power‐law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks. (shrink)

Background Journal editors are responsible for what they publish and therefore have a duty to correct the record if published work is found to be unreliable. One method for such correction is retraction of an article. Anecdotal evidence suggested a lack of consistency in journal policies and practices regarding retraction. In order to develop guidelines, we reviewed retractions in Medline to discover how and why articles were retracted. Methods We retrieved all available Medline retractions from 2005 to 2008 and a (...) one-in-three random selection of those from 1988 to 2004. This yielded 312 retractions (from a total of 870). Details of the retraction including the reason for retraction were recorded by two investigators. Results Medline retractions have increased sharply since 1980 and currently represent 0.02% of included articles. Retractions were issued by authors (63%), editors (21%), journals (6%), publishers (2%) and institutions (1%). Reasons for retraction included honest error or non-replicable findings (40%), research misconduct (28%), redundant publication (17%) and unstated/unclear (5%). Some of the stated reasons might have been addressed by corrections. Conclusions Journals' retraction practices are not uniform. Some retractions fail to state the reason, and therefore fail to distinguish error from misconduct. We have used our findings to inform guidelines on retractions. (shrink)

We describe the ongoing citations to biomedical articles affected by scientific misconduct, and characterize the papers that cite these affected articles. The citations to 102 articles named in official findings of scientific misconduct during the period of 1993 and 2001 were identified through the Institute for Scientific Information Web of Science database. Using a stratified random sampling strategy, we performed a content analysis of 603 of the 5,393 citing papers to identify indications of awareness that the cited articles affected by (...) scientific misconduct had validity issues, and to examine how the citing papers referred to the affected articles. Fewer than 5% of citing papers indicated any awareness that the cited article was retracted or named in a finding of misconduct. We also tested the hypothesis that affected articles would have fewer citations than a comparison sample; this was not supported. Most articles affected by misconduct were published in basic science journals, and we found little cause for concern that such articles may have affected clinical equipoise or clinical care. (shrink)

BackgroundIn about one in 10,000 cases, a published article is retracted. This very often means that the results it reports are flawed. Several authors have voiced concerns about the presence of retracted research in the memory of science. In particular, a retracted result is propagated by citing it. In the published literature, many instances are given of retracted articles that are cited both before and after their retraction. Even worse is the possibility that these articles in turn are cited in (...) such a way that the retracted result is propagated further.MethodsWe have conducted a case study to find out how a retracted article is cited and whether retracted results are propagated through indirect citations. We have constructed the entire citation network for this case.ResultsWe show that directly citing articles is an important source of propagation of retracted research results. In contrast, in our case study, indirect citations do not contribute to the propagation of the retracted result.ConclusionsWhile admitting the limitations of a study involving a single case, we think there are reasons for the non-contribution of indirect citations that hold beyond our case study. (shrink)

In 2009, Scott S. Reuben was convicted of fabricating data, which lead to 25 of his publications being retracted. Although it is clear that the perpetuation of retracted articles negatively effects the appraisal of evidence, the extent to which retracted literature is cited had not previously been investigated. In this study, to better understand the perpetuation of discredited research, we examine the number of citations of Reuben’s articles within 5 years of their retraction. Citations of Reuben’s retracted articles were assessed (...) using the Web of Science Core Collection. All citing articles were screened to discriminate between articles in which Reuben’s work was quoted as retracted, and articles in which his data was wrongly cited without any note of the retraction status. Twenty of Reuben’s publications had been cited 274 times between 2009 and 1024. In 2014, 45 % of the retracted articles had been cited at least once. In only 25.8 % of citing articles was it clearly stated that Reuben’s work had been retracted. Annual citations decreased from 108 in 2009 to 18 in 2014; however, the percentage of publications correctly indicating the retraction status also declined. The percentage of citations in top-25 %-journals, as well as the percentage of citations in journals from Reuben’s research area, declined sharply after 2009. Our data show that even 5 years after their retraction, nearly half of Reuben’s articles are still being quoted and the retraction status is correctly mentioned in only one quarter of the citations. (shrink)