Science is studied in very different ways by historians, philosophers, psychologists, and sociologists. Not only do researchers from different fields apply markedly different methods, they also tend to focus on apparently disparate aspects of science. At the farthest extremes, we find on one side some philosophers attempting logical analyses of scientific knowledge, and on the other some sociologists maintaining that all knowledge is socially constructed. This paper is an attempt to view history, philosophy, psychology, and sociology of science from a (...) unified perspective. (shrink)

In this era of rapid modernization, technology has changed people’s everyday lives globally but at a heavy price, as evidenced, for example, by the earth’s deteriorating environments. Environmental contamination has induced the adverse impacts of climate change, manifested as natural disasters. According to scientific predictions, if climate change continues at the current rate, irreversible damage to the planet’s ability to sustain life could occur by 2100. This disturbing scenario has prompted a wake-up call for promoting sustainability and initiatives, such as (...) the Sustainable Development Goals formulated by the United Nations, which are aimed at influencing and penetrating every aspect of life. This article discusses the importance of pragmatic wisdom for our earth’s restoration through the achievement of sustainability, which requires a revolution in education. A new educational model, particular within higher education, which extends beyond most of the current educational models for acquiring knowledge, is required to promote pragmatic wisdom. Apart from the acquisition of scientific knowledge, philosophical thinking and critical thinking skills are essential for promoting pragmatic wisdom. In this context, an education that couples liberal arts with natural sciences could be one of the solutions for facilitating the transformation of knowledge into pragmatic wisdom, which can potentially foster sustainability. (shrink)

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the (...) researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work. (shrink)

This article makes the case for more objectivity in Machine Learning (ML) research. Any research work that claims to hold benefits has to be scrutinized based on many parameters, such as the methodology employed, ethical considerations and its theoretical or technical contribution. We approach this discussion from a Naturalist philosophical outlook. Although every analysis may be subjective, it is important for the research community to keep vetting the research for continuous growth and to produce even better work. We suggest standardizing (...) some of the steps in ML research in an objective way and being aware of various biases threatening objectivity. The ideal of objectivity keeps research rational since objectivity requires beliefs to be based on facts. We discuss some of the current challenges, the role of objectivity in the two elements (product and process) that are up for consideration in ML and make recommendations to support the research community. (shrink)