As is known, the digital era brought new possibilities for creation, organization and work with large databases. However, some problems make such large databases difficult to manage, as e.g., lack of definite standards, or perhaps even full impossibility to develop any standard at all due to the complexity of the data. This certainly is the case of databases in the humanities, especially in regard to fields in which the aspect of temporality is constitutive. In this paper we discuss models for (...) organization of knowledge, with particular attention to the modern fate of the traditional ‘trees of knowledge’, the emergence of decentered network models and new possibilities that emerged together with the digital humanities. We conclude with some considerations on very recent initiatives to solve the problem posed by temporality in large textual corpora. (shrink)

According to Peter Galison, the coordination of different “subcultures” within a scientific field happens through local exchanges within “trading zones.” In his view, the workability of such trading zones is not guaranteed, and science is not necessarily driven towards further integration. In this paper, we develop and apply quantitative methods (using semantic, authorship, and citation data from scientific literature), inspired by Galison’s framework, to the case of the disunity of high-energy physics. We give prominence to supersymmetry, a concept that has (...) given rise to several major but distinct research programs in the field, such as the formulation of a consistent theory of quantum gravity or the search for new particles. We show that “theory” and “phenomenology” in high-energy physics should be regarded as distinct theoretical subcultures, between which supersymmetry has helped sustain scientific “trades.” However, as we demonstrate using a topic model, the phenomenological component of supersymmetry research has lost traction and the ability of supersymmetry to tie these subcultures together is now compromised. Our work supports that even fields with an initially strong sentiment of unity may eventually generate diverging research programs and demonstrates the fruitfulness of the notion of trading zones for informing quantitative approaches to scientific pluralism. (shrink)

Network analysis views complex systems as networks with well-defined structural properties that account for their complexity. These characteristics, which include scale-free behavior, small worlds and communities, are not to be found in networks such as random graphs and lattices that do not correspond to complex systems. They provide therefore a robust ground for claiming the existence of “complex networks” as a non-trivial subset of networks. The theory of complex networks has thus been successful in making systematically explicit relevant marks of (...) complexity in the form of structural properties, and this success is at the root of its current popularity. Much less systematic has been, on the other hand, the definition of the properties of the building components of complex networks. The obvious assumption is that these components must be nodes and links. Generally, however, the internal structure of nodes is not taken into account, and links are serendipitously identified by the perspective with which one looks at the network to be analyzed. For instance, if the nodes are Web pages that contain information about scientific papers, one point of view will match the relevant links with hyperlinks to similar Web pages, and another with citations of other articles. We intend to contribute here a systematic approach to the identification of the components of a complex network that is based on information theory. The approach hinges on some recent results arising from the convergence between the theory of complex networks and probabilistic techniques for content mining. At its core there is the idea that nodes in a complex network correspond to basic information units from which links are extracted via methods of machine learning. Hence the links themselves are viewed as emergent properties, similarly to the broader structural properties mentioned above. Indeed, beside rounding up the theory, this approach based on learning has clear practical benefits, in that it makes networks emerge from arbitrary information domains. We provide examples and applications in a variety of contexts, starting from an information-theoretic reconstruction of the well-known distinction between “strong links” and “weak links” and then delving into specific applications such as business process management and analysis of policy making. (shrink)

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to (...) the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted. (shrink)

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (...) (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin’s behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin’s own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin’s consumption more exploratory than the culture’s production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior. (shrink)