Search

O objetivo deste artigo é compreender o papel e a relevância da sociologia da ciência para as ideias desenvolvidas por Thomas Kuhn em A Estrutura das Revoluções Científicas. Neste artigo, argumento que a Estrutura propõe uma explicação sociológica para aspectos fundamentais do desenvolvimento científico, ainda que as relações entre as ideias propostas na Estrutura e a sociologia da ciência sejam pouco discutidas no livro. Em primeiro lugar, Kuhn mostra como o desenvolvimento da ciência se assenta sobre formas específicas de organização e de prática nas comunidades científicas. Em segundo lugar, sua explicação sobre as resoluções de controvérsias depende de uma abordagem necessariamente comunitária da atividade científica. Ao final do artigo, discuto a ligação entre a abordagem sociológica de Kuhn e as perspectivas atuais em epistemologia social.

Pirá is a recently developed reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change. No detailed set of baselines has been built with this dataset yet, something that certainly hinders its use by researchers. In this paper, we define five benchmarks over the Pirá dataset, covering machine reading comprehension, information retrieval, open question answering, answer triggering, and multiple choice question answering. As part of this effort, we have produced a curated version of the original dataset, where we fixed a number of grammar issues, repetitions and other shortcomings. Furthermore, the dataset, now called Pirá 2.0, has been extended in several new directions, so as to face the aforementioned benchmark tasks: translation of supporting texts into Portuguese, classification labels on answerability, multiple choice candidates, and automatic paraphrases of questions and answers. The results described in this paper provide a reference point for researchers working with Pirá 2.0. Our results show that Pirá 2.0 is indeed a very challenging dataset, particularly useful for testing the ability of current machine learning models in acquiring expert scientific knowledge.

No quarto capítulo de A estrutura das revoluções científicas, Thomas Kuhn descreve a ciência normal como uma atividade de resolução de quebra-cabeças. Entre as características que permitiriam aproximar a ciência normal dos quebra-cabeças estaria o fato de as soluções de problemas científicos precisarem seguir regras específicas, como o uso de determinadas generalizações, instrumentos, compromissos metafísicos e valores. Contudo, ao final desse mesmo capítulo e especialmente no seguinte, Kuhn rejeita o paralelo anteriormente formulado: a ciência normal, agora, não seria caracterizada pela utilização de regras, e sim guiada diretamente por paradigmas. Como conciliar essas duas visões aparentemente conflitantes sobre a natureza da ciência normal? Neste trabalho, discuto algumas explicações sobre a origem de tal tensão, em particular a descrição de Hoyningen-Huene sobre as etapas de escrita da Estrutura. A partir daí, considero uma possível solução para esta dificuldade, que aponta para o caráter público da atividade científica. Segundo argumento, os paradigmas nem sempre são capazes de fornecer critérios de decisões claros; nesse caso, as regras compartilhadas poderiam funcionar — de maneira parcial e provisória — como instrumentos para transmissão, discussão e avaliação dos paradigmas.

In The Structure of Scientific Revolutions, Thomas Kuhn resorts to concepts from several disciplines in order to describe the general patterns of scientific development. This blend of disciplines can be explained in part by Kuhn's intellectual path, from physics to history and then to philosophy of science; but it also points to a deeper methodological problem, which is the question of what is the real unity of analysis in his model of science. The primary intention of this article is, thus, to give a solution to this difficulty. The answer, I believe, rests on identifying three fundamental units present in Kuhn's theory of scientific development. They are, respectively, the individual, responsible for producing evidence, spreading information, and choosing theories; the community, a set of scientists investigating a series of phenomena; and the groups, individuals with similar behavior but with looser institutional or social ties — a usually neglected category in Kuhnian literature, but equally fundamental for the final outcome of scientific debates. After investigating these categories in detail, I propose a way of integrating them into a general model for explaining the resolution of scientific controversies. Finally, I try to resolve the apparent conflict among disciplinary vocabularies by offering an account of the function of sociological, psychological, and epistemological concepts for describing controversies, and some of the methodologies appropriate for each of these tasks. In The Structure of Scientific Revolutions, Thomas Kuhn resorts to concepts from several disciplines in order to describe the general patterns of scientific development. This blend of disciplines can be explained in part by Kuhn's intellectual path, from physics to history and then to philosophy of science; but it also points to a deeper methodological problem, which is the question of what is the real unity of analysis in his model of science. The primary intention of this article is, thus, to give a solution to this difficulty. The answer, I believe, rests on identifying three fundamental units present in Kuhn's theory of scientific development. They are, respectively, the individual, responsible for producing evidence, spreading information, and choosing theories; the community, a set of scientists investigating a series of phenomena; and the groups, individuals with similar behavior but with looser institutional or social ties — a usually neglected category in Kuhnian literature, but equally fundamental for the final outcome of scientific debates. After investigating these categories in detail, I propose a way of integrating them into a general model for explaining the resolution of scientific controversies. Finally, I try to resolve the apparent conflict among disciplinary vocabularies by offering an account of the function of sociological, psychological, and epistemological concepts for describing controversies, and some of the methodologies appropriate for each of these tasks. In The Structure of Scientific Revolutions, Thomas Kuhn resorts to concepts from several disciplines in order to describe the general patterns of scientific development. This blend of disciplines can be explained in part by Kuhn's intellectual path, from physics to history and then to philosophy of science; but it also points to a deeper methodological problem, which is the question of what is the real unity of analysis in his model of science. The primary intention of this article is, thus, to give a solution to this difficulty. The answer, I believe, rests on identifying three fundamental units present in Kuhn's theory of scientific development. They are, respectively, the individual, responsible for producing evidence, spreading information, and choosing theories; the community, a set of scientists investigating a series of phenomena; and the groups, individuals with similar behavior but with looser institutional or social ties — a usually neglected category in Kuhnian literature, but equally fundamental for the final outcome of scientific debates. After investigating these categories in detail, I propose a way of integrating them into a general model for explaining the resolution of scientific controversies. Finally, I try to resolve the apparent conflict among disciplinary vocabularies by offering an account of the function of sociological, psychological, and epistemological concepts for describing controversies, and some of the methodologies appropriate for each of these tasks.

According to a widespread view, Thomas Kuhn’s model of scientific development would relegate rationality to a second plane, openly flirting with irrationalist positions. The intent of this article is to clarify this aspect of his thinking and refute this common interpretation. I begin by analysing the nature of values in Kuhn’s model and how they are connected to rationality. For Kuhn, a theory is chosen rationally when: i) the evaluation is based on values characteristic of science; ii) a theory is considered better the more it manifests these values; and iii) the scientist chooses the best-evaluated theory. The second part of this article deals with the thesis of the variability of values. According to Kuhn, the examples through which epistemic values are presented vary for each person, and for this reason individuals interpret these criteria differently. Consequently, two scientists, using the same values, can come to a rational disagreement over which theory to choose. Finally, I point out the limitations of this notion of rationality for the explanation of consensus formation, and the corresponding demand for a sociological theory that reconnects individual rationality with convergence of opinions.

Como cientistas escolhem teorias? O objetivo de nossa tese é entender a resposta de Thomas Kuhn a este problema clássico da filosofia da ciência. Ao retirar o problema da escolha de teorias do campo metodológico e transportá-lo para o campo sociológico, Kuhn expõe os fundamentos de uma abordagem sociológica do desenvolvimento científico. Como tantos outros filósofos, Kuhn defende que teorias científicas são escolhidas a partir de valores epistêmicos critérios como precisão, consistência, abrangência, simplicidade e fecundidade. Segundo ele, porém, estes valores estão sujeitos a interpretações distintas: cientistas podem concordar quanto ao que se deve esperar de uma teoria, mas, em função de históricos pessoais e profissionais particulares, podem discordar em relação a qual delas melhor manifesta essas características. Mas se os cientistas aplicam os valores de maneiras distintas, em que sentido estes valores ditariam as suas escolhas? E, principalmente, como seria possível, dada a variabilidade dos valores, o consenso em uma comunidade de cientistas? A resposta de Kuhn, inaugurando sua abordagem sociológica, é a de que o acordo entre os membros da comunidade seria gerado por uma série de mecanismos sociais. Em primeiro lugar, a pedagogia e o treinamento dos cientistas, que tornaria as avaliações dos cientistas mais parecidas. Depois, a teoria de onda: a produção de novas evidências e argumentos responsáveis por convencer os adeptos de teorias rivais. Por último, a reestruturação da comunidade: a exclusão de membros resistentes e a divisão da comunidade em disciplinas distintas. A fim de esclarecer a natureza desta sociologia, discutiremos dois conjuntos de temas relacionados: os tipos de explicações de crença: racional e causal; e os níveis explicativos desta sociologia: indivíduos, comunidades e grupos. A última parte de nossa tese consiste em uma tentativa de sistematizar um modelo de explicação sociológica da dinâmica das controvérsias científicas, assim como apontar alguns caminhos para uma pesquisa empírica direcionada a estes tópicos. How do scientists choose theories? The aim of our thesis is to understand Thomas Kuhn\'s answer to this classic problem in Philosophy of Science. By removing the theory-choice problem from the methodological field and transporting it to the sociological field, Kuhn sets out the foundations of a sociological approach to scientific development. Like so many other philosophers, Kuhn argues that scientific theories are chosen based on epistemic values criteria such as accuracy, consistency, scope, simplicity, and fruitfulness. However, these values are, according to him, subject to different interpretations: scientists may agree on what to expect from a theory, but depending on particular personal and professional histories, they may disagree as to which theory best expresses these characteristics. But if scientists apply values in different ways, in which sense would these values dictate scientists choices? And, especially, how could a consensus in a community of scientists be achieved, given the variability of values? Kuhn\'s answer, inaugurating his sociological approach, is that agreement among community members would be generated by a series of social mechanisms. First, the pedagogy and training of scientists, which make scientists appraisals more similar. Secondly, the wave-theory: the production of new evidence and arguments that convince the followers of rival theories. Finally, the restructuring of the community: the exclusion of resistant members and the division of the community into distinct disciplines. In order to clarify the nature of this sociology, we will discuss two sets of related themes: the types of explanations of belief: rational and causal; and the explanatory levels of this sociology: individuals, communities and groups. The last part of our thesis consists of an attempt to systematize a model of sociological explanation for the dynamics of scientific controversies, as well as to point out the ways to an empirical research directed to these topics.

The Structure of Scientific Revolutions represented a milestone in the attempt to understand scientific development based on empirical observations. However, in the next decades after the publication of his book, history, psychology, and sociology became increasingly marginal in Kuhn’s discussions. In his last articles, Kuhn even suggested that philosophers should pay less attention to empirical data and focus more on “first principles.” The purpose of this article is, first, to describe this radical transformation in Kuhn’s methodological approach, from his initial naturalism to his later and more strict philosophical stance. Next, I present some of the alleged justifications for explaining this transition, such as his interest in problems more firmly attached to the philosophical tradition and a desire for greater acceptance within this community. Although these factors certainly played a role in explaining Kuhn’s change, I also believe that an important theoretical component exerts a fundamental function in this transition: the idea that scientific development is a kind of evolutionary process, which is better understood through the use of abstract theoretical models, instead of sparse observations of scientific activity.