Abstract
I review the background and some recent trends of a particular scholarly information network, arXiv.org, and discuss some of its implications for new scholarly publication models. If we were to start from scratch today to design a quality-controlled archive and distribution system for scientific and technical information, it could take a very different form from what has evolved in the past decade from pre-existing print infrastructure. Near-term advances in automated classification systems, authoring tools, and document formats will facilitate efficient datamining and long-term archival stability, and I discuss how these could provide not only more efficient means of accessing and navigating the information, but also more cost-effective means of authentication and quality control. Finally, I illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its network of practitioners.
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Ginsparg, P. Scholarly Information Network. In: Ben-Naim, E., Frauenfelder, H., Toroczkai, Z. (eds) Complex Networks. Lecture Notes in Physics, vol 650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44485-5_15
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DOI: https://doi.org/10.1007/978-3-540-44485-5_15
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