Research on bias in peer review examines scholarly communication and funding processes to assess the epistemic and social legitimacy of the mechanisms by which knowledge communities vet and self-regulate their work. Despite vocal concerns, a closer look at the empirical and methodological limitations of research on bias raises questions about the existence and extent of many hypothesized forms of bias. In addition, the notion of bias is predicated on an implicit ideal that, once articulated, raises questions about the normative implications (...) of research on bias in peer review. This review provides a brief description of the function, history, and scope of peer review; articulates and critiques the conception of bias unifying research on bias in peer review; characterizes and examines the empirical, methodological, and normative claims of bias in peer review research; and assesses possible alternatives to the status quo. We close by identifying ways to expand conceptions and studies of bias to countenance the complexity of social interactions among actors involved directly and indirectly in peer review. (shrink)

Transcranial direct current stimulation is a safe and efficient intervention for treating major depressive disorder. However, research has suggested heterogeneity of response between patients. The emerging field of precision psychiatry aims to use statistical modeling of multi-modal data to tailor treatment to the single patient. To this end, more in-depth analysis of randomized controlled trials will be relevant due to limited availability of other large datasets with high phenotypic detail and to develop tools for personalization within counterfactually controlled environments to (...) distinguish specific vs. non-specific patterns in treatment data. Previous research has aimed at identifying patient-related factors associated with better response. However, most analyses have operated on the group-level, ignoring natural clusters within the patients' constituting factors, their individual trajectories of symptom improvement, and their presented symptoms. Furthermore, group-based modeling strategies were limited to explanatory approaches using in-sample hypothesis-testing, that are ill-suited to prognosticate outcomes of single patients. This dissertation provides a methodological framework for reevaluation of existing clinical trial data to provide future investigations with more differentiated units of analysis and to complement explanatory approaches with predictive modeling strategies enabling prediction of single-patient outcomes. Using data from a landmark 3-arm clinical trial paradigmatic for a rigorously controlled experimental design the dissertation provides three blueprint studies for modeling heterogeneity of tDCS response: Study 1 characterized response to tDCS by considering patient-individual dynamics of symptom change over the course of treatment. Distinct trajectories of tDCS response could be identified, representing patient subgroups with varying strength and speed of improvement. These results suggest development of individualized treatment protocols and exploration of prolonged treatment courses. Study 2 reevaluated the efficacy of tDCS, in distinct, naturally occurring clusters of depressive symptoms. Using unsupervised machine learning, a global depression measure was parsed into 4 distinct symptom clusters. Analysis of cluster-scores showed superiority of tDCS and escitalopram over placebo in core depressive symptoms, but only tDCS was superior in improving sleep and only escitalopram was superior in improving guilt/anxiety symptoms, suggesting treatment selection based on patients' symptom profiles. In Study 3 supervised ML algorithms were employed to predict response to tDCS. In this proof-of-concept approach, response could be predicted above chance on the single-patient level, but overall accuracy was modest. Features employed for model training were explored using interpretable ML methods. Trained algorithms were provided to the field for expansion as well as tests of generalizability and incremental utility. The presented studies illustrate how in-depth secondary analyses of clinical trial data can aid personalization of treatment. The provided methodological framework can be expanded and generalized to other contexts and interventions that show heterogeneity of treatment effects. Yet, the empirical studies also epitomize challenges precision psychiatry is faced with, including low data availability, low outcome granularity, and limited external validation opportunities. The dissertation concludes with a discussion of challenges and future directions resulting from infrastructural demands in data acquisition, data management, data sharing, and interdisciplinary collaboration. (shrink)

The practice of HARKing---hypothesizing after results are known---is commonly maligned as undermining the reliability of scientific findings. There are several accounts in the literature as to why HARKing undermines the reliability of findings. We argue that none of these is right and that the correct account is a Bayesian one. HARKing can indeed decrease the reliability of scientific findings, but it can also increase it; which effect HARKing produces depends on the difference of the prior odds of hypotheses characteristically selected (...) ex ante and ex post to observing data. Further, we show how misdiagnosis of HARKing can lead to misprescription in the context of the replication crisis. (shrink)

Understanding modeling in biology requires understanding how biology is organized as a discipline and how this organization influences the research practices of biologists. Biology includes a wide range of sub-disciplines, such as cell biology, population biology, evolutionary biology, molecular biology, and systems biology among others. Biologists in sub-disciplines such as cell, molecular, and systems biology believe that the use of a few experimental models allows them to discover biological universals, whereas biologists in sub-disciplines such as ecology and evolutionary biology believe (...) that the use of many different experimental and mathematical models is necessary in order to do this. Many practitioners of both approaches misunderstand best practices of modeling, especially those related to model testing. We stress the need for biologists to better engage with best practices and for philosophers of biology providing normative guidance for biologists to better engage with current developments in biology. This is especially important as biology transitions from a “data-poor” to a “data-rich” discipline. If 21st century biology is going to capitalize on the unprecedented availability of ecological, evolutionary, and molecular data, of computational resources, and of mathematical and statistical tools, biologists will need a better understanding of what modeling is and can be. (shrink)

Scientific research is based on the division of cognitive labour: every scientist has to trust that other colleagues have checked whether the items that are taken as knowledge, and she cannot check by herself, are reliable enough. I apply ideas from the field known as ‘information economics’ to analyse the scientists’ incentives to produce items of knowledge of an ‘adequate’ quality, under the assumption that a big part of what one observes in her empirical research is not available for the (...) readers of the paper. I also discuss some criticisms to this ‘marketization’ of science studies. (shrink)

In July 2014, the roboticist Ronald Arkin suggested that child sex robots could be used to treat those with paedophilic predilections in the same way that methadone is used to treat heroin addicts. Taking this onboard, it would seem that there is reason to experiment with the regulation of this technology. But most people seem to disagree with this idea, with legal authorities in both the UK and US taking steps to outlaw such devices. In this paper, I subject these (...) different regulatory attitudes to critical scrutiny. In doing so, I make three main contributions to the debate. First, I present a framework for thinking about the regulatory options that we confront when dealing with child sex robots. Second, I argue that there is a prima facie case for restrictive regulation, but that this is contingent on whether Arkin’s hypothesis has a reasonable prospect of being successfully tested. Third, I argue that Arkin’s hypothesis probably does not have a reasonable prospect of being successfully tested. Consequently, we should proceed with utmost caution when it comes to this technology. (shrink)

Internal mechanisms that uphold the reliability of published scientific results have failed across many sciences, including some that are major sources of science news. Traditional methods for reporting science in the mass media do not effectively compensate for this unreliability. I argue for a new conceptual framework in which science journalists and scientists form a complex knowledge community, with science news as the interdisciplinary product. This approach motivates forms of collaboration and training that can improve the epistemic reliability of science (...) news. (shrink)

Several scholars, including Martin Hengel, R. Alan Culpepper, and Richard Bauckham, have argued that Papias had knowledge of the Gospel of John on the grounds that Papias’s prologue lists six of Jesus’s disciples in the same order that they are named in the Gospel of John: Andrew, Peter, Philip, Thomas, James, and John. In “A Note on Papias’s Knowledge of the Fourth Gospel” (JBL 129 [2010]: 793–794), Jake H. O’Connell presents a statistical analysis of this argument, according to which the (...) probability of this correspondence occurring by chance is lower than 1%. O’Connell concludes that it is more than 99% probable that this correspondence is the result of Papias copying John, rather than chance. I show that O’Connell’s analysis contains multiple mistakes, both substantive and mathematical: it ignores relevant evidence, overstates the correspondence between John and Papias, wrongly assumes that if Papias did not know John he ordered the disciples randomly, and conflates the probability of A given B with the probability of B given A. In discussing these errors, I aim to inform both Johannine scholarship and the use of probabilistic methods in historical reasoning. (shrink)

Many natural scientists of the past and the present have imagined that they pursued their activity according to its own inherent rules in a realm distinctly separate from the business world, or at least in a realm where business tended to interfere with science from time to time, but was not ultimately an essential component, ‘because one thought that in science one possessed and loved something unselfish, harmless, self-sufficient, and truly innocent, in which man’s evil impulses had no part whatever’, (...) as Nietzsche once commented (Nietzsche 1974, p.106). With the extreme technological changes that have occurred in the last fifty years and the orchestrated management changes in the culture of science, it is now obvious that science is intimately tied to private businesses, industry, and society. Within this structure, the scientist has generally unknowingly defined him or herself in accordance with obsolete myths that have tended to handicap the scientist’s freedom of action, by obscuring the modern political and economic realities of science, and neglecting the inherent responsibilities of the scientist as a critical actor in the theater of human civilization. The increasing incorporation of academic science and private industry, and the governmentally supervised nature of modem academic science, has corrupted the traditional freedom and character of the scientist. In order to navigate oneself and find meaning within the new structure of science, scientists now desperately need a fresh ethos that at once considers modern realities of the politics and management of science, societal urgencies, and global politics, as well as establishing a moral perspective where modern scientists can be actors with their own intentionality and responsibility. -/- In the culture of science, myths and ideologies are of critical importance for the formation of the scientist, because these ideas determine how scientists conceive of themselves as professionals and free individuals. More importantly, these ideas determine how scientists approach scientific activity, which exists in a social context, and which ultimately has the potential to dramatically change the characteristics of civilization as it is played out on the political and technological battlefield. It is a commonly held notion in the community of the natural sciences that science now is essentially what it was when it was described by the Nobel laureates of the past; those sweet, cushioning, pleasant words consecrating the ‘temple of science’; the picture of a humble, rational, and noble Einstein is imprinted into our memories. By dismantling these obsolete myths of the scientist and surveying contemporary trends in science, this article will explore a more realistic perspective toward the field of science. The goal of this investigation is to determine the modern reality and ultimate meaning of ‘science as a vocation’ (Shorett 2003). Finally, moral philosophy will be demonstrated to be a critical clement in the self-assertion of the scientist and the elucidation of the meaning of science as a vocation in a global technological society. -/- From the Postscript, 2015: This article took final form when I was a postdoctoral researcher in Canada. Reading it now, I recognize the article still has value, despite its many flaws, hyperboles, and idealism. Many issues in the article have become increasingly relevant, such as the need to make constant trade-offs in courses of action and continuously search for more morally significant research paths. Perhaps the greatest goals in writing the article were to increase intellectual control over the path of my scientific research, to escape the increasing conformity and constraints on modern life, to participate in an open society, to formulate my personal goals and goal hierarchies, to dispel any delusions of overconfidence, and to not be corrupted. (shrink)

Hierarchies of evidence are an important and influential tool for appraising evidence in medicine. In recent years, hierarchies have been formally adopted by organizations including the Cochrane Collaboration [1], NICE [2,3], the WHO [4], the US Preventive Services Task Force [5], and the Australian NHMRC [6,7]. The development of such hierarchies has been regarded as a central part of Evidence-Based Medicine, a movement within healthcare which prioritises the use of epidemiological evidence such as that provided by Randomised Controlled Trials. Philosophical (...) work on the methodology of medicine has so far mostly focused on claims about the superiority of RCTs, and hence has largely neglected the questions of what hierarchies are, what assumptions they require, and how they affect clinical practice. This thesis shows that there is great variation in the hierarchies defended and in the interpretations they are, and can be, given. The interpretative assumptions made in using hierarchies are crucial to the content and defensibility of the underlying philosophical commitments concerning evidence and medical practice. Once this variation is been identified, it becomes clear that the little philosophical work that has been done so far affects only some hierarchies, under some interpretations. Modest interpretations offered by La Caze [11], conditional hierarchies like GRADE [12-14], and heuristic approaches such as that defended by Howick et al. [15,16] all survive previous philosophical criticism. This thesis extends previous criticisms by arguing that modest interpretations are so weak as to be unhelpful for clinical practice; that GRADE and similar conditional models omit clinically relevant information, such as information about variation in treatments’ effects and the causes of different responses to therapy; and that heuristic approaches lack the necessary empirical support. The conclusion is that hierarchies in general embed untenable philosophical assumptions: principally that information about average treatment effects backed by high-quality evidence can justify strong recommendations, and that the impact of evidence from individual studies can and should be appraised in isolation. Hierarchies are a poor basis for the application of evidence in clinical practice. The Evidence-Based Medicine movement should move beyond them and explore alternative tools for appraising the overall evidence for therapeutic claims. (shrink)

Once an experiment is done, the observations have been made and the data have been analyzed, what should scientists communicate to the world at large, and how should they do it? This, I will argue, is an intricate question, and one that philosophers can make a contribution to. I will illustrate these points by reference to the debate between Fisher and Neyman & Pearson in the 1950s, which I take to be, at heart, a debate about norms of scientific communication. (...) I will argue that scientists need a richer set of tools for communicating epistemic states that may be very nuanced, and will point to way in which philosophers can contribute. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced (...) in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

Principalele probleme cu care se confruntă oamenii de știință în lucrul cu seturile mari de date (Big Data), evidențiind principale aspecte etice, luând în considerare inclusiv legislația din Uniunea Europeană. După o scurtă Introducere despre Big Data, secțiunea Tehnologia prezintă aplicațiile specifice în cercetare. Urmează o abordare a principalelor probleme filosofice specifice în Aspecte filosofice, și Aspecte legale cu evidențierea problemelor etice specifice din Regulamentul UE privind protecția datelor 2016/679 (General Data Protection Regulation, "GDPR"). Secțiunea Probleme etice detaliază aspectele specifice (...) generate de Big Data. După o scurtă secțiune Cercetarea Big Data, finalizez lucrarea cu prezentarea Concluziilor pentru etica cercetării în lucrul cu Big CUPRINS: Abstract 1. Introducere - 1.1 Definiții - 1.2 Dimensiunile Big Data 2. Tehnologia - 2.1 Aplicații - - 2.1.1 În cercetare 3. Aspecte filosofice 4 Aspecte legale - 4.1 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - Paradoxul (legal) al Big Data 5. Probleme etice - Etica în cercetare - Conștientizarea - Consimțământul - Controlul - Transparența - Încrederea - Proprietatea - Supravegherea și securitatea - Identitatea digitală - Realitatea ajustată - De-anonimizarea - Inegalitatea digitală - Confidențialitatea 6. Cercetarea Big Data Concluzii Bibliografie DOI: 10.13140/RG.2.2.27629.33761 . (shrink)

As it became clear that Donald Trump had a real base of political support, even as analysts consistently underestimated his electoral prospects, they grew increasingly fascinated with the question of who was supporting him (and why). However, researchers also tend to hold strong negative opinions about Trump. Consequently, they have approached this research with uncharitable priors about the kind of person who would support him and what they would be motivated by. Research design and data analysis often seem to be (...) oriented towards reinforcing those assumptions. This essay highlights the epistemological consequences of these tendencies through a series of case studies featuring prominent and influential works that purport to explain the role of race and racism in the 2016 U.S. presidential election. It demonstrates that quality control systems, which should catch major errors, seem to be failing in systematic ways as a result of shared priors and commitments between authors, reviewers and editors – which are also held in common with the journalists and scholars citing and amplifying this work – leading to misinformation cascades. Of course, motivated reasoning, confirmation bias, prejudicial study design, and failure to address confounds are not limited to questions about Trump – however they seem to be particularly pronounced in this case due to the relative homogeneity and intensity of scholars’ views about this topic as compared to other social phenomena. “Trump studies,” therefore, provides fertile ground for exploring how social research can go awry – and the consequences of these failures -- particularly with respect to work on contentious and politically-charged topics. (shrink)

“Open access” has become a central theme of journal reform in academic publishing. In this article, I examine the consequences of an important technological loophole in which publishers can claim to be adhering to the principles of open access by releasing articles in proprietary or “locked” formats that cannot be processed by automated tools, whereby even simple copy and pasting of text is disabled. These restrictions will prevent the development of an important infrastructural element of a modern research enterprise, namely, (...) scientific data science, or the use of data analytic techniques to conduct meta-analyses and investigations into the scientific corpus. I give a brief history of the open access movement, discuss novel journalistic practices, and an overview of data-driven investigation of the scientific corpus. I argue that particularly in an era where the veracity of many research studies has been called into question, scientific data science should be one of the key motivations for open access publishing. The enormous benefits of unrestricted access to the research literature should prompt scholars from all disciplines to reject publishing models whereby articles are released in proprietary formats or are otherwise restricted from being processed by automated tools as part of a data science pipeline. (shrink)

The “reproducibility crisis” has been a highly visible source of scientific controversy and dispute. Here, I propose and review several avenues for identifying and prioritizing research studies for the purpose of targeted validation. Of the various proposals discussed, I identify scientific data science as being a strategy that merits greater attention among those interested in reproducibility. I argue that the tremendous potential of scientific data science for uncovering high-value research studies is a significant and rarely discussed benefit of the transition (...) to a fully open-access publishing model. (shrink)

[ES] Este trabajo, realiza una sumarísima revisión histórica, a través de algunas conjeturas epistemológicas, sobre las reflexiones filosóficas acerca del mundo del arte, su estatuto, desarrollo y «funcionamiento» con el propósito de allanar el camino para una ulterior descripción de la problemática acerca de la noción del mundo del arte. [EN] This writing makes a brief historical review, through some epistemological assumptions, on the philosophical reflections on the art world, its status, development and «functioning» in order to pave the way (...) for a further description of the problems about the notion of the art world. (shrink)

This chapter describes the potential far‐reaching consequences of contemplative higher education for the fields of science and medicine.

Humans clearly have trouble thinking about death. This trouble is often used to explain behavior like delay in writing wills or buying life insurance, or interest in odd medical and religious beliefs. But the problem is far worse than most people imagine. Fear of death makes us spend fifteen percent of our income on medicine, from which we get little or no health benefit, while we neglect things like exercise, which offer large health benefits.