Manuela Fernández Pinto

Desde los inicios del filosofar y de las ciencias, el ejercicio de controvertir ha estado presente para dinamizar la emergencia de ideas, el reconocimiento de hechos y la formulación de métodos que los seres humanos han articulado con el propósito de entender las realidades que los rodean. Sin embargo, el estudio sistemático del desacuerdo en las ciencias como problema en sí mismo es de muy reciente data. Incluso la dimensión histórica de las controversias fue eclipsada por otras cuestiones aparentemente más relevantes en los procesos de transformación que ha tenido la filosofía de la ciencia en los últimos 150 años.

El objetivo principal de este artículo es analizar los estudios que respaldan la aprobación y el uso del BiDil, el primer medicamento aprobado para uso exclusivo en población afroamericana en EE. UU., así como la controversia que surgió alrededor de este. En particular, mostraré que la controversia sobre el BiDil no debe entenderse como una controversia puramente epistémica, sino como una controversia compleja en la que convergen aspectos tanto epistémicos como ético-sociales y comerciales. Entender cómo están interrelacionados estos aspectos y qué conocimiento podemos obtener dada la complejidad de estas interacciones resulta crucial para comprender la controversia sobre el BiDil, así como otras controversias científicas actuales.

Philosophers of science have argued that epistemic diversity is an asset for the production of scientific knowledge, guarding against the effects of biases, among other advantages. The growing privatization of scientific research, on the contrary, has raised important concerns for philosophers of science, especially with respect to the growing sources of biases in research that it seems to promote. Recently, Holman and Bruner ( 2017 ) have shown, using a modified version of Zollman ( 2010 ) social network model, that an industrial selection bias can emerge in a scientific community, without corrupting any individual scientist, if the community is epistemically diverse. In this paper, we examine the strength of industrial selection using a reinforcement learning model, which simulates the process of industrial decision-making when allocating funding to scientific projects. Contrary to Holman and Bruner’s model, in which the probability of success of the agents when performing an action is given a priori, in our model the industry learns about the success rate of individual scientists and updates the probability of success on each round. The results of our simulations show that even without previous knowledge of the probability of success of an individual scientist, the industry is still able to disrupt scientific consensus. In fact, the more epistemically diverse the scientific community, the easier it is for the industry to move scientific consensus to the opposite conclusion. Interestingly, our model also shows that having a random funding agent seems to effectively counteract industrial selection bias. Accordingly, we consider the random allocation of funding for research projects as a strategy to counteract industrial selection bias, avoiding commercial exploitation of epistemically diverse communities.

Arguments discrediting the value-free ideal of science have left us with the question of how to distinguish desirable values from biases that compromise the reliability of research. In this paper, I argue for a characterization of cognitive biases as deviations of thought processes that systematically lead scientists to the wrong conclusions. In particular, cognitive biases could help us understand a crucial issue in science today: how systematic error is introduced in research outcomes, even when research is evaluated as of good quality. To conclude, I suggest that some debiasing mechanisms have great potential for countering implicit methodological biases in science.

The shooting bias hypothesis aims to explain the disproportionate number of minorities killed by police. We present the evidence mounting in support of the existence of shooting bias and then focus on two dissenting studies. We examine these studies in light of Biddle and Leuschner’s “inductive risk account of epistemically detrimental dissent” and conclude that, although they meet this account only partially, the studies are in fact epistemically and socially detrimental as they contribute to racism in society and to a social atmosphere that is hostile to science as scholars working on issues of racism come under attack. We emphasize this final point via recourse to Kitcher’s “Millian argument against the freedom of research.”

The shooting bias hypothesis aims to explain the disproportionate number of minorities killed by police. We present the evidence mounting in support of the existence of shooting bias and then focus on two dissenting studies. We examine these studies in light of Biddle and Leuschner’s “inductive risk account of epistemically detrimental dissent” and conclude that, although they meet this account only partially, the studies are in fact epistemically and socially detrimental as they contribute to racism in society and to a social atmosphere that is hostile to science as scholars working on issues of racism come under attack. We emphasize this final point via recourse to Kitcher’s “Millian argument against the freedom of research.”

Gender bias is a recalcitrant problem in academia and society. However, dissent has been created on this issue. We focus on dissenting studies by Stephen J. Ceci and Wendy M. Williams, arguing that they reach conclusions that are unwarranted on the basis of the available evidence and that they ignore fundamental objections to their methodological decisions. Drawing on discussions from other contexts, particularly on manufactured dissent concerning anthropogenic climate change, we conclude that dissent on gender bias substantially contributes to the exacerbation of biases in society and an increasing number of attacks on researchers, making it both epistemically and socially problematic.

Weisberg and Muldoon’s epistemic landscape model (ELM) has been one of the most significant contributions to the use of agent-based models in philosophy. The model provides an innovative approach to establishing the optimal distribution of cognitive labor in scientific communities, using an epistemic landscape. In the paper, we provide a critical examination of ELM. First, we show that the computing mechanism for ELM is correct insofar as we are able to replicate the results using another programming language. Second, we show that small changes in the rules that determine the behavior of individual agents can lead to important changes in simulation results. Accordingly, we claim that ELM results are robust with respect to the computing mechanism, but not necessarily across parameter space. We conclude by reflecting on the possible lessons to be gained from ELM as a class of simulations or cluster of models

The aim of this chapter is to examine some of the key contributions of feminist philosophers of science to the study of ignorance. First, I provide a brief introduction to agnotology and its critical stance to traditional epistemology. Then, I illustrate how the study of ignorance can serve as a tool for feminist epistemology through an examination of case studies. In the third section, I examine the importance of ignorance studies for the feminist project in philosophy of science. Finally, in the fourth section I suggest some new directions worth pursuing in this area.

The aim of the paper is to clarify the concept of scientific ignorance: what is it, what are its sources, and when is it epistemically detrimental to science. While some sources of scientific ignorance come inevitably with the process of knowledge acquisition, others are deliberately created. The former includes selection processes, inductive reasoning, and cognitive biases, while the latter includes scientific fraud. Another important source of scientific ignorance appears when scientists introduce methodological biases through micro-decisions in the research process. I provide three examples from medical research to illustrate this point. I argue further that methodological biases present a challenge, in so far as they are no easily classifiable as deliberate: they might also be the result of entrenched research practices within a scientific community. Strategies to identify and prevent methodological biases in research should take into account such difference.

A coherent and helpful public policy based on science is difficult to achieve for at least three reasons. First, there are purely practical problems—for example, that scientific experts often disagree on policy-relevant questions and their debates often continue well beyond policy appropriate timelines. Second, there are epistemic problems—for example, that science is hardly the neutral supplier of factual information that traditionally has been supposed. And third, there are social problems: given the commercialization of today’s science and its enduring limitations, much of scientific research today fails to meet the moral and political standards one would expect it to meet in order to inform public policy. In this paper, we examine such problems in the context of breast cancer screening policy and suggest the role philosophy of science should play in dealing with the situation.

Due to its allegedly diverse population and strong doctor–patient relations, Latin America has become one of the most attractive locations for international clinical trials. In the paper, I examine the case of recruitment of women and minority patients to serve as subjects of international clinical trials, through CROs operating in Latin America. In particular, the paper examines some of the strategies that CROs use to expand their services in the Latin American medical market, illuminating the mechanisms through which the current organization of medical research contributes to power imbalances in the Global South. After analyzing the epistemic and ethical shortcomings of such endeavor, I show how Latin American patients participating in clinical trials are located in a position of double disadvantage. First, they suffer the consequences of a lack of appropriate understanding of symptoms and reaction to treatment. Second, they suffer the consequences of being subjects in clinical trials which are not designed to meet their needs, but the needs of patients in the Global North. Accordingly, I conclude by highlighting the importance of this double disadvantage and suggesting that the problem can be understood in terms of a misalignment of commercial, ethical, and epistemic concerns in clinical research.

The growing body of research on interdisciplinarity has encouraged a more in depth analysis of the relations that hold among academic disciplines. In particular, the incursion of one scientific discipline into another discipline's traditional domain, also known as scientific imperialism, has been a matter of increasing debate. Following this trend, Scientific Imperialism aims to bring together philosophers of science and historians of science interested in the topic of scientific imperialism and, in particular, interested in the conceptual clarification, empirical identification, and normative assessment of the idea of scientific imperialism. Thus, this innovative volume has two main goals. Indeed, the authors first seek to understand interdisciplinary relations emerging from the incursion of one scientific discipline into one or more other disciplines, such as in cases in which the conventions and procedures of one discipline or field are imposed on other fields; or more weakly when a scientific discipline seeks to explain phenomena that are traditionally considered proper of another discipline's domain. Secondly, the authors explore ways of distinguishing imperialistic from non-imperialistic interactions between disciplines and research fields. The first sustained study of scientific imperialism, this volume will appeal to postgraduate students and postdoctoral researchers interested in fields such as Science and Technology Studies, Sociology of Science & Technology, Philosophy of Science, and History of Science.

Philosophers of science have become increasingly concerned with the social dimensions of scientific knowledge. The general aim of the paper is to show that approaches in social epistemology of science fail to take into account important changes that the organization of science has undergone in the past decades. I argue that the social organization of science is an important “social dimension” of scientific knowledge that philosophers need to consider. In order to do so, I focus on Helen Longino’s social epistemology of science as portrayed in her critical contextual empiricism. I show that Longino’s approach has important limitations when trying to implement it as a guide for current research through an evaluation of two of her norms of effective criticism.

The aim of the paper is to clarify the concept of scientific ignorance: what is it, what are its sources, and when is it epistemically detrimental for science. I present a taxonomy of scientific ignorance, distinguishing between intrinsic and extrinsic sources. I argue that the latter can create a detrimental epistemic gap, which have significant epistemic and social consequences. I provide three examples from medical research to illustrate this point. To conclude, I claim that while some types of scientific ignorance are inevitable and even desirable, other types of scientific ignorance are epistemically and ethically flawed and should be prevented.

The article examines the process of niche standardization in medical research as an example of democratizing strategies implemented in industry-funded science. I argue that niche standardization can lead to undesirable epistemic and ethical consequences, if the various goals of research are not properly aligned. I examine two examples: the case of Sarafem, approved for the treatment of premenstrual dysphoric disorder in women, and the case of BiDil, approved for exclusive use in African Americans for the treatment of congestive heart failure. I conclude by cautioning against the unqualified support of democratizing strategies in industry-funded research.

The purpose of this paper is to uncover some of the limitations that critical contextual empiricism, and in particular Longino's contextualism, faces when trying to provide a normative account of scientific knowledge that is relevant to current scientific research. After presenting the four norms of effective criticism, I show how the norms have limited scope when dealing with cases of current scientific practices. I then present some historical evidence for the claim that the organization of science has changed in recent decades, and I argue that the uncovered limitations emerge from this larger phenomenon. Finally, I conclude by suggesting two ways to overcome the previously uncovered limitations.

With an innovative perspective on the social character of ignorance production, agnotology has been a fruitful approach for understanding the social and epistemological consequences of the interaction between industry and scientific research. In this paper, I argue that agnotology, or the study of ignorance, contributes to a better understanding of commercially driven research and its societal impact, showing the ways in which industrial interests have reshaped the epistemic aims of traditional scientific practices, turning them into mechanisms of ignorance production. To do so, I examine some of the main contributions to agnotology and provide a taxonomy of practices of ignorance construction common in commercially driven research today. In particular, I present the tobacco industry’s campaign against the health hazards of smoking as a paradigmatic case of ignorance production, identifying five central strategies. I then argue that the same strategies have been used in three other cases—global warming, pharmaceuticals, and the 2008 financial crisis.

The growing body of research on interdisciplinarity has encouraged a more in depth analysis of the relations that hold among academic disciplines. In particular, the incursion of one scientific discipline into another discipline’s traditional domain, also known as scientific imperialism, has been a matter of increasing debate. Following this trend, Scientific Imperialism aims to bring together philosophers of science and historians of science interested in the topic of scientific imperialism and, in particular, interested in the conceptual clarification, empirical identification, and normative assessment of the idea of scientific imperialism. Thus, this innovative volume has two main goals. Indeed, the authors first seek to understand interdisciplinary relations emerging from the incursion of one scientific discipline into one or more other disciplines, such as in cases in which the conventions and procedures of one discipline or field are imposed on other fields; or more weakly when a scientific discipline seeks to explain phenomena that are traditionally considered proper of another discipline’s domain. Secondly, the authors explore ways of distinguishing imperialistic from non-imperialistic interactions between disciplines and research fields.

In recent decades, philosophers of science have become increasingly concerned with the social dimensions of scientific knowledge. Philosophers such as Helen Longino, Philip Kitcher, Miriam Solomon, Heather Douglas, and Janet Kourany have sought to incorporate the social aspects of science, while retaining the normative commitments of philosophy of science. Some of the major theoretical approaches in social epistemology of science, however, tend to ignore or underestimate the role that the current state of science organization plays in the production of scientific knowledge.1 The world has changed significantly in recent decades, and science has changed with it. The..

Expanding on recent philosophical contributions to the conceptual and normative framework of scientific imperialism, I examine whether the economics approach to social epistemology can be considered a case of economics imperialism and determine whether economics’ explanatory expansionism appropriately contributes to this philosophical subfield or not. I argue first that the economics approach to social epistemology counts as a case of economics imperialism under a broad conception of the term, and second that we have good reasons to doubt the appropriateness of the incursion of economics into social epistemology, insofar as economics’ attempt at explanatory unification fails to express significant human interests.

As scientific research moves increasingly to the private sector, the social organization of science undergoes important transformations. Focusing on the production of ignorance, agnotology has been a fruitful approach to understanding the social and epistemic consequences of the recent commercialization of scientific research. Despite their important contributions, scholars working on agnotology seem to hold implicit normative commitments that are in tension with their descriptive accounts of ignorance-constructive practices. The main aim of this article is to uncover these commitments and to expose the emerging tensions. Thus, this article begins an exploration into normative aspects of the studies of ignorance. In particular, it shows that agnotology still needs the support of a well-articulated normative approach capable of identifying and evaluating the epistemic and social concerns raised by the private funding and performance of science.

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold.