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Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study

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Abstract

We propose new methods to detect paradigmatic fields through simple statistics over a scientific content database. We propose an asymmetric paradigmatic proximity metric between terms which provide insight into hierarchical structure of scientific activity and test our methods on a case study with a database made of several millions of resources. We also propose overlapping categorization to describe paradigmatic fields as sets of terms that may have several different usages. Terms can also be dynamically clustered providing a high-level description of the evolution of the paradigmatic fields.

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Authors and Affiliations

  1. TSV (Social and Political Transformations related to Life Sciences and Life Forms, INRA, Ivry sur Seine, France

    Jean-Philippe Cointet

  2. Center for Research in Applied Epistemology (CREA), Team Complex Systems, Adaptive Rationality and Social Cognition, Ecole Polytechnique, 1, rue Descartes, 75005, Paris, France

    David Chavalarias

Authors
  1. David Chavalarias
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  2. Jean-Philippe Cointet
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Correspondence to David Chavalarias.

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Chavalarias, D., Cointet, JP. Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study. Scientometrics 75, 37–50 (2008). https://doi.org/10.1007/s11192-007-1825-6

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  • DOI: https://doi.org/10.1007/s11192-007-1825-6

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