A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and (...) others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief and action. (...) The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world. (shrink)

This paper investigated the part played by collaborative practices in chaneling the work of prominent biochemists into the development of molecular biology. The RNA collaborative network that emerged in the 1960s in France encompassed a continuum of activities that linked laboratories to policy-making centers. New institutional frameworks such as the DGRST committees were instrumental in establishing new patterns of funding, and in offering arenas for multidisciplinary debates and boundary assessment. It should be stressed however, that although this collaborative network was (...) based on centralized initiatives aimed at developing molecular biology as a new biological specialty, it operated above all as a nexus of practices. The main argument of this paper is that the central allocation of funds and resources, exemplified by the DGRST operation, actually enhanced the creation of a self-conscious community of biochemists turned molecular biologists by virtue of an increased circulation of tools, skills, and results that took place within the RNA network and a few analogous systems of exchange.Having hands on “things” viewed as identical for all practical purposes was a potent factor in changing the experimental systems and their meanings. Limited but shared means of doing helped to reduce uncertainties, change representations, and turn contingent decisions into meaningful choices. The collaborative enterprises then resulted in personal contacts and the transfer of skills and materials, which gradually incorporated the biochemical tools into systems producing facts relevant to molecular biology as defined by its early practitioners. In that sense, networking was a regulatory process that stabilized new research objects and acculturated French biochemists.The mere existence of such a collaborative network also changed the scale of the disciplining process. Collaborations may have been started for contingent motives, but multiple exchanges resulted in the emergence of a new collective, and amplified small displacements. Collaborations, however, worked both ways, and the RNA network may be viewed as an efficient “trading-post.” An unexpected outcome of the development of a conversion zone is the fact that, by the late 1960s, the former biochemists dominated the “new” world of molecular biology — both in terms of research habits, since interests in structural studies dominated the field, and in terms of institutional initiatives such as the creation of laboratories and institutes for molecular biology.As an example of the cognitive displacements achieved by the network, I have focused here on the stabilization of “messenger RNA” as a new biological entity. This process illustrates the role of “boundary objects” and other mediating innovations in the development of disciplinary structures. Students of science trained in the symbolic interactionism tradition have proposed that “boundary objects” enhance the multiple interactions between heterogeneous social worlds: they are robust enough to enhance unity, but plastic enough to be manipulated in different social and cultural contexts.81 Within the emerging network, messenger RNA was a weakly structured “genetic information carrier” in common use, but it could, at the same time, be a strongly structured “macromolecular structure” adapted to practical and local uses. Consequently, messenger RNA favored the association of groups of heterogeneous scientists with backgrounds and interests in medical biochemistry, genetics, physical chemistry, organic chemistry, and so forth. This contrast between general and local uses was also instrumental in integrating the manipulation of things and the negotiation of aims. In contrast to transfer RNAs, which in the French context remained objects for chemical (and mainly structural) studies, messenger RNA became a key component of the new culture of “genetic information”. Messenger RNA was a loose theoretical entity described as a “genetic information carrier” in the policy-making documents, while operational but tacit and more conflicting definitions prevailed at the bench. In other words, messenger RNA was not only a classical “boundary object” but also a “flag object,” which tightened the collaborative network by mediating between the DGRST offices and the laboratories. (shrink)

Developing World Bioethics, Volume 22, Issue 3, Page 126-139, September 2022.

This article examines the communication networks within and between science and technology studies (STS) and the history of science. In particular, journal relatedness data are used to analyze some of the structural features of their disciplinary identities and relationships. The results first show that, although the history of science is more than half a century older than STS, the size of the STS network is more than twice that of the history of science network. Further, while a majority of (...) the journals in the STS network are connected by weak ties, about half of the history of science network consists of strong ties. The history of science network is thus more cohesive than the STS network. The relatively strong cohesion within the history of science network is associated with comparatively high degrees of intra-disciplinary communication, but comparatively weak ties to only a few related disciplines. The analysis also shows that very few members of the history of science cliques are situated on the shortest path between both specialties. Moreover, given the relatively impermeable nature of the history of science network, the latter partially depends on STS to reach some of the neighboring disciplines. (shrink)

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what (...) instantiates that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. (shrink)

Our scientific theories, like our cognitive structures in general, consist of propositions linked by evidential, explanatory, probabilistic, and logical connections. Those theoretical webs ‘impinge on the world at their edges,’ subject to a continuing barrage of incoming evidence. Our credences in the various elements of those structures change in response to that continuing barrage of evidence, as do the perceived connections between them. Here we model scientific theories as Bayesian nets, with credences at nodes and conditional links between (...) them modelled as conditional probabilities. We update those networks, in terms of both credences at nodes and conditional probabilities at links, through a temporal barrage of random incoming evidence. Robust patterns of punctuated equilibrium, suggestive of ‘normal science’ alternating with ‘paradigm shifts,’ emerge prominently in that change dynamics. The suggestion is that at least some of the phenomena at the core of the Kuhnian tradition are predictable in the typical dynamics of scientific theory change captured as Bayesian nets under even a random evidence barrage. (shrink)

Paul Churchland's philosophical work enjoys an increasing popularity. His imaginative papers on cognitive science and the philosophy of psychology are widely discussed. Scientific Realism and the Plasticity of Mind (1979), his major book, is an important contribution to the debate on realism. Churchland provides us with the intellectual tools for constructing a unified scientific Weltanschauung. His network theory of language implies a provocative view of the relation between science and common sense. This paper contains a critical examination of (...) Churchland's network theory of language, which is the foundation of his philosophy. It is argued that the network theory should be seen as deriving its point from traditional empiricism. The network theory enables the empiricist to resist the phenomenalistic temptations inherent in his position, and to build a realist philosophy on the basis of the representative theory of perception. This interpretation is confirmed by the fact that the representative theory is presupposed by Churchland's main argument in favour of the network view. Churchland tends to conceive of himself as a naturalistic epistemologist. But the philosophical faction to which Churchland belongs is rather that of modern neo?Kantianism. (shrink)

The analysis of social networks has remained a crucial and yet understudied aspect of the efforts to measure Triple Helix linkages. The Triple Helix model aims to explain, among other aspects of knowledge-based societies, “the current research system in its social context” (Etzkowitz and Leydesdorff 2000:109). This paper develops a novel approach to study the research system from the perspective of the individual, through the analysis of the relationships among researchers, and between them and other social actors. We develop (...) a new set of techniques and show how they can be applied to the study of a specific case (a group of academics within a university department). We analyse their informal social networks and show how a relationship exists between the characteristics of an individual’s network of social links and his or her research output. (shrink)

Based on a national survey of Chinese scientific personnel in 2008, this paper sheds new light on the relationship between social networks and scientific performance. In this study, we used position generator to measure scientists’ ego-centered social networks. The scientists’ performance was measured by multiple indexes, including recognitions from the academic, governmental, and market sectors. The findings show that size and composition of scientists’ social networks have significant effect on their scientific performance. The notions (...) of “information communication mechanism” and “resource acquisition mechanism” are introduced to explain how network composition affects scientific performance along multiple dimensions. The policy implications of the study are also discussed. (shrink)

Aiming to improve the commercialization efficiency of scientific innovative achievements, this paper utilizes the time series visualization method to construct the time series network of each subsystem. After that, the network similarity is calculated by the cosine similarity theorem. On this basis, a new multilayer network adjacency matrix is obtained. With the adoption of k-core technology, the critical nodes can be identified to study the transformation efficiency of the innovation value in the network. Finally, according to the provincial innovation (...) value transformation data of China from 1998 to 2016, an empirical study was carried out to calculate and analyze the transformation efficiency of innovation achievements in 30 provinces. The results indicate that the transformation efficiency of innovation value can be expressed by the structure of the time series network constructed by the input-output vectors; the mapping relationship of the value transformation vectors could be reflected by the cosine similarity of the time series network, while the transformation efficiency of innovation value could be identified using the k-core; and the transformation efficiency of innovation value in three coastal provinces is relatively higher, while that of the rest of the provinces is roughly the same among the 30 provinces. (shrink)

Empirical studies such as Goyal et al. (J Polit Econ 114(2):403–412, 2006) or Newman (Proc Natl Acad Sci USA 101(Suppl. 1):5200–5205, 2004) show that scientific collaboration networks present a highly unequal and hierarchical distribution of links. This implies that some researchers can be much more active and productive than others and, consequently, they can enjoy a much better scientific reputation. One may think that big intrinsical differences among researchers can constitute the main driving force behind these inequalities. (...) Nevertheless, this model shows that, under specific circumstances, very similar individuals may self-organize themselves forming unequal and hierarchical structures. (shrink)

This book addresses the various systems in place for collaborative e-research and how these practices serve to enhance the quality of research across disciplines, covering new networks available through social media as well as traditional methods such as mailing lists and forums.

Research programs regularly compete to achieve the same goal, such as the discovery of the structure of DNA or the construction of a TEA laser. The more the competing programs share information, the faster the goal is likely to be reached, to society’s benefit. But the “priority rule”-the scientific norm according to which the first program to reach the goal in question must receive all the credit for the achievement-provides a powerful disincentive for programs to share information. How, then, (...) is the clash between social and individual interest resolved in scientific practice? This chapter investigates what Robert Merton called science’s “communist” norm, which mandates universal sharing of knowledge, and uses mathematical models of discovery to argue that a communist regime may be on the whole advantageous and fair to all parties, and so might be implemented by a social contract that all scientists would be willing to sign. (shrink)

Scientific networks are often credited with bringing about institutional change and professional advancement, but less attention has been paid to their instability and occasional failures. In the 1970s optimism among academic women was high as changing US policies on sex discrimination in the workplace, including higher education, seemed to promise equity. Encouraged by colleagues, Shyamala Rajender charged the University of Minnesota with sex discrimination when it failed to consider her for a tenure-track position. The widely cited case of (...) this chemist was not, however, settled easily and involved nearly a decade of university grievance procedures and litigation that grew to a class action lawsuit. As the case gained national attention and internal resistance stiffened, the clusters of women who had been encouraging flickered, faded and sometimes regrouped. A negotiated settlement (consent decree) ended Rajender’s case, and it opened the door for hundreds of others to present their grievances regarding gender discrimination. Networks and support groups proved important but also unstable for individuals who sought equity before and during the implementation of the decree. The Rajender case thus exposes the painful, balky and inevitably contentious process of fighting discrimination. It also demonstrates the power and limits of institutions and litigation, as well as the possibilities and disappointments of informal and formal women’s networks. (shrink)

In Artificial Intelligence, Artificial Neural Networks are very accurate models in tasks such as classification and regression in the study of natural phenomena, but they are considered “black boxes” because they do not allow direct explanation of what they address. This paper reviews the possibility of scientific explanation from these models and concludes that other efforts are required to understand their inner workings. This poses challenges to access scientific explanation through their use, since the nature of Artificial (...) Neural Networks makes it difficult at first instance the scientific understanding that can be extracted from them. (shrink)

Using data on the ‘career’ paths of one thousand ‘leading scientists’ from 1450 to 1900, what is conventionally called the ‘rise of modern science’ is mapped as a changing geography of scientific practice in urban networks. Four distinctive networks of scientific practice are identified. A primate network centred on Padua and central and northern Italy in the sixteenth century expands across the Alps to become a polycentric network in the seventeenth century, which in turn dissipates into (...) a weak polycentric network in the eighteenth century. The nineteenth century marks a huge change of scale as a primate network centred on Berlin and dominated by German-speaking universities. These geographies are interpreted as core-producing processes in Wallerstein’s modern world-system; the rise of modern scientific practice is central to the development of structures of knowledge that relate to, but do not mirror, material changes in the system. (shrink)

In this study, we sorted out the research hotspots in sports science by bibliometric method and also used social network analysis to explore the relationship between knowledge networks and their scientific performance. We found 38 high-frequency keywords with obvious curricular nature or classical direction of sports science research and 4 high-frequency research groups. The topics of hotspots covered the secondary disciplines of sports science: physical education and training, national traditional sports, sports human science, and sports humanities and sociology. (...) However, sports human science research is less; therefore, accelerating the research of sports human science is the focus of future research. Meanwhile, we use social network structure analysis to study the relationship between knowledge elements in knowledge networks and their scientific performance. In addition to betweenness centrality, the closeness centrality, clustering coefficient, and structural holes of knowledge elements are significantly and positively related to their influence. In the relationship between knowledge elements and productivity, betweenness centrality and closeness centrality show significant positive correlations, and clustering coefficient and structural hole show significant negative correlations. Therefore, knowledge networks can be used to predict the scientific performance of knowledge elements. (shrink)

The paper discusses the role of networks in cognition on two levels: on the level of the organization of ideas, and on the level of interpersonal communication. Any interesting system of ideas forms a network: ideas presented in a linear order (the order forced upon us by verbal expression) will necessarily convey a distorted picture of the underlying patterns of thought. Networks of ideas typically consist of a great number of nodes with just a few links, and a (...) small number of hubs with very many links; that is, they are, to employ Albert-László Barabási's term, "scale-free." Barabási fits into a specific tradition: Hungarians had an early influence on the philosophy of networks, and on the philosophy of communication as developed at Marshall McLuhan's Toronto Circle. In fact, this was the circle in which certain Hungarian and Austrian ideas on mediated collective thinking first came together—a telling testimony to the conditions of disturbed communication and idiosyncratic networking typical of East-Central Europe, past and present. The nodes-and-hubs pattern is characteristic, too, of social networks, in particular of scholarly and scientific networks. The paper analyses the role of "invisible colleges"—informal groups of scientific elites through whom the communication of information both within a field and across fields is channelled. By way of conclusion the notion of a new type of personality, the "network individual," is discussed: the network individual is the person reintegrated, after centuries of relative isolation induced by the printing press, into the collective thinking of society—the individual whose mind is manifestly mediated, once again, by the minds of those forming his/her smaller or larger community. (shrink)

Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are (...) not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. (shrink)

In his mature works, Kuhn abandons the concept of a paradigm and becomes more interested in the analysis of the conceptual structure of scientific theories. These changes are interpreted as resulting from a ‘linguistic turn’ that Kuhn underwent sometimes around the 1980s. Much of the philosophical discussions about Kuhn’s post-‘linguistic turn’ philosophy revolves around his views on taxonomic concepts. Apart from taxonomy, however, the mature Kuhn introduces other concepts, such as conceptual networks and lexicons. This article distinguishes these (...) three concepts and shows that adopting each one of them has different impacts on Kuhn’s model of science. After distinguishing and assessing the concepts of taxonomy, network, and lexicon, it will be argued that the latter not only fits the Kuhnian model of science better, but it also helps capture the wide sense of Kuhn’s early concept of a paradigm. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates (...) theorizing about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage (...) and analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures. (shrink)

Recent research in philosophy of science has shown that scientists rely on a plurality of strategies to develop successful explanations of different types of phenomena. In the case of biology, most of these strategies go far beyond the traditional and reductionistic models of scientific explanation that have proven so successful in the fundamental sciences. Concretely, in the last two decades, philosophers of science have discovered the existence of at least two different types of scientific explanation at work in (...) the biological sciences, namely: mechanistic and structural explanations. Despite the growing evidence about the radically different nature of these two types of explanation, no inquiry has been conducted to date to determine the ontological reasons that might underlie these differences, nor the way in which these types of explanations can be systematically related with each other. Here, we aim to cover this gap by connecting this plurality of research strategies with the existence of emergent levels of reality. We argue that the existence of these different—and apparently incompatible—explanatory strategies to account for biological phenomena derives from the existence of “ontological jumps” in nature, which generate different regimes of causation that in turn demand the development of different explanatory frameworks. We identify two of these strategies—mechanistic modelling and network modelling—and connect them to the existence of two ontological regimes of causation. Finally, we relate them with each other in a systematic way. In this vein, our paper provides an ontological justification for the plurality of explanatory strategies that we see in the life sciences. (shrink)

In this study, we explore a 100 years of Work and Organizational Psychology. To do this, we carry out a bibliometric performance and network analysis to understand the evolution structure and the most important themes in the field of study. To perform the BNPA, 8,966 documents published since 1919 were exported from the Web of Science and Scopus databases. The SciMAT software was used to process data and to create the evolution structure, the strategic diagram, and the thematic network structure (...) of the strategic themes of the field of WOP. We identified 29 strategic clusters and discuss the most important themes and their relationship with other clusters. This research presents the complete evolution of the field of study, identifying emerging themes and others with a high degree of development. We hope that this work will support researchers and future research in the field of WOP. (shrink)

In this paper I discuss the ontological status of actants. Actants are argued as being the basic constituting entities of networks in the framework of Actor Network Theory (Latour, 2007). I introduce two problems concerning actants that have been pointed out by Collin (2010). The first problem concerns the explanatory role of actants. According to Collin, actants cannot play the role of explanans of networks and products of the same newtork at the same time, at pain of circularity. (...) The second problem is that if actants are, as suggested by Latour, fundamentally propertyless, then it is unclear how they combine into networks. This makes the nature of actants inexplicable. -/- I suggest that both problems rest on the assumption of a form of object ontology, i.e. the assumption that the ontological basis of reality consists in discrete individual entities that have intrinsic properties. I argue that the solution to this problem consists in the assumption of an ontology of relations, as suggested within the framework of Ontic Structural Realism (Ladyman & Ross, 2007). Ontic Structural Realism is a theory concerning the ontology of science that claims that scientific theories represent a reality consisting on only relation, and no individual entities. -/- Furthermore I argue that the employment of OSR can, at the price of little modification for both theories, solve both of the two problems identified by Collin concerning ANT. -/- Throughout the text I seek support for my claims by referring to examples of application of ANT to the context of networked learning. As I argue, the complexity of the phenomenon of networked learning gives us a convenient vantage point from which we can clearly understand many important aspects of both ANT and OSR. -/- While my proposal can be considered as an attempt to solve Collin's problems, it is also an experiment of reconciliation between analytic and constructivist philosophy of science. -/- In fact I point out that on the one hand Actor Network Theory and Ontic Structural Realism show an interesting number of points of agreement, such as the naturalistic character and the focus on relationality. On the other hand, I argue that all the intuitive discrepancies that originates from the Science and Technology Studies’ criticism against analytic philosophy of science are at a closer look only apparent. (shrink)

Jean Ayres invented Sensory Integration for children experiencing learning and social difficulties because, according to Ayres, they could not adequately integrate information from multiple sensory modalities. She established a scientific basis for her identification of children with sensory integrative difficulties, using statistical techniques to identify symptoms and neuroscience to determine a cause. She was an unusually reflective practitioner who catalyzed a community of practice around SI without becoming a guru—indeed, she encouraged her students to come up with their own (...) ideas and test them empirically. She felt isolated from the growing field of Occupational Therapy yet is viewed as one of its greatest pioneers. After her death in 1988, the SI community gradually began to argue about fundamental issues like what should constitute an appropriate diagnosis and set of tests for SI. At present, the network is fragmented to the point where some of the opposing positions may be incommensurable with each other, which would require a trading zone. (shrink)

In France between 1780 and 1815, doctors opened a broad correspondence with medical faculties and public officials about foetal anomalies . Institutional and legal reforms forced doctors to encounter monstrous births with greater frequency, and they responded by developing new ideas about heredity and embryology to explain malformations to public officials. Though doctors achieved consensus on pathogenesis, they struggled to apply these ideas in forensic cases, especially with doubtful sex. Medical networks simultaneously allowed doctors to explore obstetrical techniques, as (...) licensing regulations forced practitioners into emotional encounters with child anomalies. Doctors thus developed a new ethics for treating monstrosities, viewing them as pathological specimens, forensic objects, and obstetrical tragedies. (shrink)

Network theory is applied across the sciences to study phenomena as diverse as the spread of SARS, the topology of the cell, the structure of the Internet and job search behaviour. Underlying the study of networks is graph theory. Whether the graph represents a network of neurons, cells, friends or firms, it displays features that exclusively depend on the mathematical properties of the graph itself. However, the way in which graph theory is implemented to the modelling of networks (...) differs significantly across scientific fields. This article compares the economics variant of network theory with those of other fields. It shows how the methodology employed by economists to model networks is shaped by two explanatory desiderata: that the explanandum phenomenon is based on micro-economic foundations and that the explanation is general. (shrink)

Traditional historical scholarship struggles to keep up with the rapid pace of modern scientific publication trends. Even focusing on a particular scientific field, the rate of new publications far outpaces even the most studious historian’s research capacity. This essay summarizes an approach to this problem that uses computational techniques of network analysis. As a complement to close analysis of particular documents, network analysis can give a large-scale perspective on the history of science, identifying relational patterns across a vast (...) number of documents that might otherwise require an entire career to digest. To demonstrate the power of the approach, the essay applies network theory to a corpus of publications in evolutionary medicine. Four distinct networks, including those focused on authors, keywords, and citations, quickly unearth a range of relevant historical information. The essay illustrates how interpretable historical conclusions are drawn from a variety of quantitative metrics. The aim is to provide an overview of network techniques for historians looking to add robust network analysis to their research repertoire. (shrink)

The most common way of managing cultural heritage recently takes form of cultural routes as they seem to offer a new model of participation in culture to their recipients; they are often a peculiar anchor point for inhabitants to let them understand their identity and form the future; they offer actual tours to enter into interaction with culture and history, to build together that creation of the heritage, which so is becoming not only a touristic product, but, first of all, (...) the space for cultural, social and civic activity. Yet, so far, according to what we know, the research problem concerning the method of cultural route organization into solid structures or more of the networked nature, has not been deliberated. A question arises, what values are brought by routes and how to organize routes to be the carriers of the values important for communities, where routes are functioning. And, as a consequence, if, from the point of view of the values of local communities, organizing solid route structures or organizing more widely-spaced, network-based routes would bring effects and what those effects would be. Thus, the posed question is of course scientifically imprecise because a network is a type of structure but presents a given direction for the development of cultural route structures. Our objective here is to present a certain solidity and rigidity of structure with dynamic and smooth understanding of the network. The research presented in the article is based on 3 case studies. We have selected for this purpose the three largest cultural routes in Poland, organized to various degrees. The outcome of the research was referred also to other cultural route organization research. (shrink)

When surprising events occur, people naturally try to generate explanations of them. Such explanations usually involve hypothesizing causes that have the events as effects. Reasoning from effects to prior causes is found in many domains, including: Social reasoning: when friends are acting strange, we conjecture about what might be bothering them. Legal reasoning: when a crime has been committed, jurors must decide whether the prosecution's case gives a convincing explanation of the evidence. Medical diagnosis: given a set of symptoms, a (...) physician tries to decide what disease or diseases produced them. Fault diagnosis in manufacturing: when a piece of equipment breaks down, a trouble shooter must try to determine the cause of the breakdown. Scientific theory evaluation: scientists seek an acceptable theory to explain experimental evidence. What is the nature of such reasoning? (shrink)

Artificial neural networks (ANNs) are new mathematical techniques which can be used for modelling real neural networks, but also for data categorisation and inference tasks in any empirical science. This means that they have a twofold interest for the philosopher. First, ANN theory could help us to understand the nature of mental phenomena such as perceiving, thinking, remembering, inferring, knowing, wanting and acting. Second, because ANNs are such powerful instruments for data classification and inference, their use also leads (...) us into the problems of induction and probability. Ever since David Hume expressed his famous doubts about induction, the principles of scientific inference have been a central concern for philosophers. (shrink)

For centuries, herbarium specimens were the focus of exchange in global botanical networks. The aim was the ‘complete’ registration of the flora, for which ‘complete’ collections in botanical institutions worldwide were considered to be a necessary basis, although this ardently sought-after ideal was never achieved. The study of colonial plants became a special priority of botanical research in the metropolises. With knowledge of the many treasures of the plant world considered the key to securing wealth and power, political and (...) economic interests influenced both the organization and the subject matter of scientific research. After the German Reich began annexing colonies in the 1880s, legal regulations established Berlin's botanical institutions as the research centre on colonial flora. They also became a clearing house for plant material from overseas. Berlin-based curators selected duplicates of herbarium specimens from the German colonies, distributing them to other botanical institutions throughout Germany. More importantly, duplicates became a form of currency in trans-imperial networks, which relied on reciprocity. In exchange for duplicate German colonial herbarium specimens, the Berlin institutions received vast quantities of botanical samples from their British, Dutch, French and American counterparts. (shrink)

Scientific collaboration can only be understood along the epistemic and cognitive grounding of scientific disciplines. New scientific discoveries in astrophysics led to a major restructuring of the elite network of astrophysics. To study the interplay of the epistemic grounding and the social network structure of a discipline, a mixed-methods approach is necessary. It combines scientometrics, quantitative network analysis and visualization tools with a qualitative network analysis approach. The centre of the international collaboration network of astrophysics is demarcated (...) by identifying the 225 most productive astrophysicists. For the years 1998–1999 and 2001–2006 four co-authorship networks are constructed comprehending each a two year period. A visualization of the longitudinal network data gives first hints on the structural development of the network. The network of 2005–2006 is analyzed in depth. Based on cohesion analysis tools for network analysis, two main cores and three smaller ones are identified. Scientists in each core and additionally in structurally interesting positions are identified and 17 qualitative expert interviews are conducted with them. The visualization of the network of 2005–2006 is used in the interviews as a stimulus for the interviewees. An analysis of the three most often used keywords of the 225 astrophysicists is included and combined with the other data. The triangulation of these approaches shows that major epistemic changes in astrophysics, e.g. the discovery of the accelerating expansion of the universe, together with technical and organizational innovations, leads to a restructuring of the network of the discipline. The importance of a combination of qualitative and quantitative network analysis tools for the understanding of the interplay of cognitive and social structure in the sociology of science is substantiated. (shrink)

I’ve been both fascinated and distressed by the arguments raging over how best to respond to the covid-19 pandemic. In particular, I’ve been struck by the way people claim scientific authority for their confident assurances of what needs to be done. And I’m especially intrigued by the scorn they often lavish on those who hold differing views on what science is telling us. The heat generated by the resulting debates is strikingly similar to the heat generated by debates over (...) the science connected with human-caused climate change. And in both cases, the disputants too often presuppose indefensibly naïve views about scientific authority and certitude, apparently unaware that even the allegedly most obvious logical truths lack the certainty attributed to scientific authority in these debates. As a rule, I dislike re-circulating my Editorials, but I think it’s time to resurrect one (modestly tweaked) from a few years ago, addressing precisely this issue (Vol. 31, No. 3, pp. 379–386, 2017). …………………………………… “Man is a rational animal who always loses his temper when he is called upon to act in accordance with the dictates of reason.” --Oscar Wilde I’ve often noticed how debates within the SSE community sometimes parallel debates in the political arena, perhaps especially with respect to the passion they elicit and the intolerance and condescension sometimes lavished on members of the “opposition.” Occasionally, of course, the debates in the SSE are nearly indistinguishable from those in the political arena—say, over the evidence for human-caused climate change. But what I find most striking is how the passion, intolerance, etc.—perhaps most often displayed by those defending whatever the “received” view happens to be—betrays either a surprising ignorance or else a seemingly convenient lapse of memory, one that probably wouldn’t appear in less emotionally-charged contexts. What impassioned partisans tend to ignore or forget concerns (a) the tentative nature of both scientific pronouncements and knowledge claims generally (including matters ostensibly much more secure than those under debate), as well as (b) the extensive network of assumptions on which every knowledge claim rests. So I’d like to offer what I hope will be a perspective-enhancer, concerning how even our allegedly most secure and fundamental pieces of a priori knowledge are themselves open to reasonable debate. A widespread, but naïve, view of logic is that no rational person could doubt its elementary laws. But that bit of popular “wisdom” is demonstrably false. And if that’s the case, then so much the worse for the degree of certitude we can expect in more controversial arenas. Let me illustrate with a few examples.[1] [1] I’m indebted to Aune, 1970 for much of what follows. (shrink)

This paper advances a framework for modeling the component interactions between cognitive and social aspects of scientific creativity and technological innovation. Specifically, it aims to characterize Innovation Networks; those networks that involve the interplay of people, ideas and organizations to create new, technologically feasible, commercially-realizable products, processes and organizational structures. The tri-partite framework captures networks of ideas (Concept Level), people (Individual Level) and social structures (Social-Organizational Level) and the interactions between these levels. At the concept level, (...) new ideas are the nodes that are created and linked, kept open for further investigation or closed if solved by actors at the individual or organizational levels. At the individual level, the nodes are actors linked by shared worldviews (based on shared professional, educational, experiential backgrounds) who are the builders of the concept level. At the social-organizational level, the nodes are organizations linked by common efforts on a given project (e.g., a company–university collaboration) that by virtue of their intellectual property or rules of governance constrain the actions of individuals (at the Individual Level) or ideas (at the Concept Level). After describing this framework and its implications we paint a number of scenarios to flesh out how it can be applied. (shrink)

Our purpose in the present work is to survey some of the formulations that the theme of networks has received in contemporary philosophy of science over a period spanning twelve decades, from the end of the 19th century up to the present time. The proposal advanced herein is to interpret the evolution of this theme in four stages: first, one that goes from a metaphor or expressive image to a notion aspiring at implementation, but still having a virtual character, (...) in the orthodox view of theories; second, a shift towards networks viewed as explicit, with the notion still having an enormous metaphorical power, as in Neurath and Quine; third, from this towards a concept amenable to implementation, as in Thagard, but still in search of an epistemological rationale, supposedly provided by BonJour and Dancy; and finally, as a notion fully endowed with a precise structuralist implementation, in the scope of the structuralist metatheory of Balzer-Moulines-Sneed. The interpretive framework adopted in this exercise in the history of philosophy of science is a modified version of Gerald Holton's thematic model, adapted to the history of philosophy instead of its original domain, the history of scientific ideas. In our version of the thematic model, we posit two main dimensions for the appraisal of any given philosophical concept, besides the thematic dimension: the metaphorical one and the dimension of its systematic implementation. (shrink)

The Norwegian Association for Heredity Research played a key role in the rise of genetics as a research field in Norway. The immediate background of its establishment in 1919 was the need for an organization that could clarify scientific issues regarding eugenics and coordinate Norwegian representation in the organized international eugenics movement. The Association never assumed this role. Instead, Norway was represented in the international eugenics movement by the so-called Norwegian Consultative Eugenics Commission, whose leader, Jon Alfred Mjøen, was (...) dismissed as a pseudo-scientist by Norwegian geneticists. The paper explores the Association’s role in defining and delimiting scientific expert knowledge in the field of genetics and eugenics in Norway. It demonstrates how struggles about academic authority on the national arena were intertwined with struggles about representation and impact in the international eugenics movement and how transnational scientific networks where mobilized to legitimize and delegitimize notions about Nordic race supremacy, racial mixing and the politics of eugenic sterilizations. (shrink)

Despite the importance of network analysis in biological practice, dominant models of scientific explanation do not account satisfactorily for how this family of explanations gain their explanatory power in every specific application. This insufficiency is particularly salient in the study of the ecology of the microbiome. Drawing on Coyte et al. (2015) study of the ecology of the microbiome, Deulofeu et al. (2021) argue that these explanations are neither mechanistic, nor purely mathematical, yet they are substantially empirical. Building on (...) their criticisms, in the present work we make a step further elucidating this kind of explanations with a general analytical framework according to which scientific explanations are ampliative, specialized embeddings (ASE), which has recently been successfully applied to other biological subfields. We use ASE to reconstruct in detail the Coyte et al.’s case study and on its basis, we claim that network explanations of the ecology of the microbiome, and other similar explanations in ecology, gain their epistemological force in virtue of their capacity to embed biological phenomena in non-accidental generalizations that are simultaneously ampliative and specialized. (shrink)

ABSTRACT In our current moment, there is considerable interest in networks, in how people and things are connected. This essay outlines one approach that brings together insights from actor-network theory, social network analysis, and digital history to interpret past scientific activity. Multispecies network analysis (MNA) is a means of understanding the historical interactions among scientists, institutions, and preferred experimental animals. A reexamination of studies of sexual behavior funded by the Committee for Research in Problems of Sex between the (...) 1920s and the 1940s demonstrates the applicability of MNA to clarifying the relations that sustained this area of psychology. The measures of weighted degree and betweenness can highlight which nodes (whether organisms or institutions) were particularly “central” to this network. Rats featured as the animals most widely studied during this period, but the analysis also reveals distinct institutional and disciplinary cultures where different species were favored as either surrogates for humans or representatives of more general biological groups. (shrink)

To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods—termed ’property descriptors’—that illuminate not (...) just the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding. (shrink)