The geography of scientific citations

Science’s main norms prescribe scientists to use citations as acknowledgements of cognitive content irrespective of geographical location. Previous studies, however, suggested that there is a considerable geographical bias in scientific citations. We argue that this geographical bias does not, in itself, falsify the notion that citations reflect acknowledgement of cognitive content, because cognitively related knowledge may be geographically concentrated as well. We analyse the role of organizational, regional and national co-location on citation likelihood for 5.5 million article pairs, and find that the geographical bias in citations is weak once cognitive relatedness is accounted for. Furthermore, we find that the effect of co-location on citation likelihood is strongest at the organizational level, weaker at the regional level, and weakest at the national level. In addition, we show that geographical co-location particularly increases the citation likelihood between two papers when knowledge relatedness between articles is low, suggesting that interdisciplinary research benefits most from co-location. Finally, we find that, when knowledge relatedness is high, the effect of geographical co-location on citation likelihood is non-existent. We discuss the implications regarding policies aimed to discourage strategic citations and to foster interdisciplinary research.

[1]  W. Hagstrom The scientific community , 1966 .

[2]  Pierre Azoulay,et al.  Matthew: Effect or Fable? , 2012, Manag. Sci..

[3]  Lene Koch,et al.  The Ethos of Science , 2002 .

[4]  Megan MacGarvie,et al.  How well do patent citations measure flows of technology? Evidence from French innovation surveys , 2005 .

[5]  Michael A. Zaggl,et al.  Manipulation of explicit reputation in innovation and knowledge exchange communities: The example of referencing in science , 2017 .

[6]  Henry Small,et al.  Cited Documents as Concept Symbols , 1978 .

[7]  Ronald N. Kostoff,et al.  The use and misuse of citation analysis in research evaluation , 1998, Scientometrics.

[8]  S. Baldi Normative versus social constructivist processes in the allocation of citations : A network-analytic model , 1998 .

[9]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[10]  R. Boschma Proximity and Innovation: A Critical Assessment , 2005 .

[11]  Martin Meyer,et al.  What is Special about Patent Citations? Differences between Scientific and Patent Citations , 2000, Scientometrics.

[12]  G. Nigel Gilbert Referencing as Persuasion , 1977 .

[13]  Rebecca Henderson,et al.  Public & Private Spillovers, Location and the Productivity of Pharmaceutical Research , 2006 .

[14]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[15]  M. M. Kessler,et al.  An experimental study of bibliographic coupling between technical papers (Corresp.) , 1963, IEEE Trans. Inf. Theory.

[16]  S. Breschi,et al.  Mobility of Skilled Workers and Co-Invention Networks: An Anatomy of Localized Knowledge Flows , 2009 .

[17]  A. F. J. Van Raan,et al.  Cognitive Resemblance and Citation Relations in Chemical Engineering Publications. , 1995 .

[18]  Julian J. Faraway,et al.  Extending the Linear Model with R , 2004 .

[19]  Christian Catalini,et al.  The incidence and role of negative citations in science , 2015, Proceedings of the National Academy of Sciences.

[20]  Jasjit Singh,et al.  Collaborative Networks as Determinants of Knowledge Diffusion Patterns , 2005, Manag. Sci..

[21]  C. Bazerman Changing Order: Replication and Induction in Scientific Practice , 1989 .

[22]  Ron Boschma,et al.  Scientific Knowledge Dynamics and Relatedness in Bio-Tech Cities , 2014 .

[23]  Norman M Kaplan,et al.  The norms of citation behavior: Prolegomena to the footnote , 1965 .

[24]  Koen Frenken,et al.  Geography of scientific knowledge: A proximity approach , 2020, Quantitative Science Studies.

[25]  Carlo Giupponi,et al.  Co-Authorship and Bibliographic Coupling Network Effects on Citations , 2014, PloS one.

[26]  Ajay Agrawal,et al.  Not Invented Here? Innovation in Company Towns , 2009 .

[27]  M. Gittelman,et al.  Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations , 2006, The Review of Economics and Statistics.

[28]  H. Bathelt,et al.  Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation , 2004 .

[29]  Jian Wang,et al.  Bias Against Novelty in Science: A Cautionary Tale for Users of Bibliometric Indicators , 2015 .

[30]  Santo Fortunato,et al.  World citation and collaboration networks: uncovering the role of geography in science , 2012, Scientific Reports.

[31]  A. Rodríguez‐Pose,et al.  Nothing is in the Air , 2016 .

[32]  Paolo Malighetti,et al.  Self-citations as strategic response to the use of metrics for career decisions , 2017, Research Policy.

[33]  Lee Fleming,et al.  Special Issue on Design and Development: Recombinant Uncertainty in Technological Search , 2001, Manag. Sci..

[34]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[35]  Susan Leigh Star,et al.  Changing Order: Replication and Induction in Scientific Practice by H. M. Collins (review) , 1988, Technology and Culture.

[36]  A. Torre On the Role Played by Temporary Geographical Proximity in Knowledge Transmission , 2008 .

[37]  Bo Jarneving,et al.  Bibliographic coupling and its application to research-front and other core documents , 2007, J. Informetrics.

[38]  Paul Nightingale,et al.  Technological capabilities, invisible infrastructure and the un-social construction of predictability: the overlooked fixed costs of useful research , 2004 .

[39]  A. Venables,et al.  Buzz: face-to-face contact and the urban economy , 2004 .

[40]  F. Malerba,et al.  Knowledge-relatedness in firm technological diversification , 2003 .

[41]  Wolfgang Glänzel,et al.  A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level , 2005, Scientometrics.

[42]  Wolfgang Glänzel,et al.  Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes , 2015, Scientometrics.

[43]  Jerome K. Vanclay,et al.  Factors affecting citation rates in environmental science , 2013, J. Informetrics.

[44]  Weimao Ke,et al.  Mapping the diffusion of scholarly knowledge among major U.S. research institutions , 2006, Scientometrics.

[45]  R. Merton The Matthew Effect in Science , 1968, Science.

[46]  C. Matthiessen,et al.  World Cities of Scientific Knowledge: Systems, Networks and Potential Dynamics. An Analysis Based on Bibliometric Indicators , 2010 .

[47]  D. Livingstone Putting science in its place , 2000, Nature.

[48]  Christoph Bartneck,et al.  Detecting h-index manipulation through self-citation analysis , 2010, Scientometrics.

[49]  P. David,et al.  Toward a new economics of science , 1994 .

[50]  Harold Maurice Collins,et al.  Tacit Knowledge, Trust and the Q of Sapphire , 2001 .

[51]  Paul A. David,et al.  The explicit economics of knowledge codification and tacitness , 2000 .

[52]  Maryann Feldman,et al.  THE LOCATIONAL DYNAMICS OF THE US BIOTECH INDUSTRY: KNOWLEDGE EXTERNALITIES AND THE ANCHOR HYPOTHESIS , 2003 .

[53]  R. Tijssen,et al.  Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe , 2010 .

[54]  Margherita Balconi,et al.  The “codification debate” revisited: a conceptual framework to analyze the role of tacit knowledge in economics , 2007 .

[55]  Y. Gingras,et al.  Cities and the geographical deconcentration of scientific activity: A multilevel analysis of publications (1987–2007) , 2014 .