A Simulation of the Structure of Academic Science (original) (raw)
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Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas is reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages.To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying scale-invariant patterns in the evolution of scientific fields, and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, M...
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We present a model in which scientists compete with each other in order to acquire status for their publications in a two-step-process: first, to get their work published in better journals, and second, to get this work cited in these journals. On the basis of two Maxwell-Boltzmann type distribution functions of source publications we derive a distribution function of citing publications over source publications. This distribution function corresponds very well to the empirical data. In contrast to all observations so far, we conclude that this distribution of citations over publications, which is a crucial phenomenon in scientometrics, is not a power law, but a modified Bessel-function.
arXiv (Cornell University), 2016
In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. ! 2! ! 5! Bibliographic coupling networks, that connect fields that cite a similar literature. The Research Space is not based on citations and connects fields when researchers are likely to have published in both of them. Beyond citation-based maps, scholars have also used online searchers to connect academic disciplines. The Clickstream Science Map by 10 connects academic disciplines based on the probability that a scholar who searched for a paper from one field, also searched for a paper from another field. In spirit, the clickstream map is similar to the networks created from co-citations or bibliographic coupling because it also focuses on knowledge flows. Yet since online searches are a more common expression of interest in a topic than a formal citation (the latter requires the costly process of publication), efforts like clickstream help leverage new datasets that are more dynamic than those based on citations. But what are these science maps used for? A common use of knowledge flow maps is to categorize knowledge. The idea of knowledge categorization has a long tradition in bibliometrics, going back at least to the work of Paul Otlet, the creator of the Universal Decimal Classification, and Ramon Llull, the creator of the XIV century science tree. This idea, however, continues to be influential in recent projects, such as the consensus Map of Science 11 or the UCSD Science Map and Classification System 3 . The UCSD science map has been used to construct a classification of 554 research areas that some university libraries now use to understand the research production of their scholars. Another example of the use of science maps includes the cross-citation maps of Leydesdorff and Rafols 5 , who overlaid the research structure of universities 12 to contextualize a university's research output. Science maps can also be powerful policy instruments. In a world where research budgets are constrained, and the probability of succeeding in a field is uncertain, science promotion agencies (like the N.