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A bibliometric analysis of collaboration in a medical specialty

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Abstract

Investigating the relationships found in the documentation of a subject field is one method of examining the communication taking place in that field. Bibliometrics provides an objective method for this type of investigation. Coauthorship, while intuitively seeming to indicate strong communication links, nevertheless has been shown to produce graphical structures that vary with changes in threshold. Having determined that clustering structure does exist in the data, preferred partitions are identified as those least likely to have occurred by chance. Further analysis is made to test that the preferred or “meaningful” structures produced from the coauthor relationship do indeed correspond with empirical evidence of “meaning”. A small dataset of 371 authors and 550 coauthor pairs is used to investigate correspondence between experimental structures and empirical evidence. Results show that components of the experimental structures are largely consistent with subject content groups as determined by index terms. Geographic focus accounts for about half the cases showing term overlap. Hence, we have some evidence that bibliometric structures determined from the coauthor relationship may be consistent with networks of communication. If this continues to be documented by further research, bibliometric analysis of coauthor relationships found in the scholarly communication of a subject area can become a basic tool for communication research.

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Notes and references

  1. A. Pritchard, Statistical bibliography or bibliometrics?Journal of Documentation, 25 (1969) 348.

    Google Scholar 

  2. D. De Solla Price, D. de B. Beaver, Collaboration in an invisible college,American Psychologist, 21 (1966) 1011–1018.

    Google Scholar 

  3. S. Crawford, Informal communication among scientists in sleep research,Journal of the American Society for Information Science, 22 (1971) 301–310.

    Google Scholar 

  4. D. Crane,Invisible Colleges: Diffusion of Knowledge in Scientific Communities, Chicago, University of Chicago Press, 1972.

    Google Scholar 

  5. N. C. Mullins,Theories and Theory Groups in Contemporary American Sociology, New York, Harper and Row, 1973.

    Google Scholar 

  6. E. Garfield, Citation analysis as a tool in journal evaluation,Science, 178 (1972) 476.

    Google Scholar 

  7. H. Small, Co-citation in the scientific literature: A new measure of the relationship between two documents,American Documentation, 14 (1963) 10–25.

    Google Scholar 

  8. H. Small, B. C. Griffith, The structure of scientific literatures I, II,Science Studies, 4 (1974) 17–40.

    Google Scholar 

  9. M. M. Kessler, Bibliographic coupling between scientific papersAmerican Documentation, 14 (1963) 10–25.

    Google Scholar 

  10. H. D. White, B. C. Griffith, Author cocitation: A literature measure of intellectual structure,Journal of the American Society for Information Science, 32 (1981) 163–171.

    Google Scholar 

  11. W. M. Shaw, Jr, Information theory and scientific communication,Scietometrics, 3 (1981) 235–249.

    Google Scholar 

  12. E. L. Logan, W. M. Shaw, Jr, An Investigation of the co-author graph,Journal of the American Society for Information Science, 38 (1987) 262–268.

    Google Scholar 

  13. S. Crawford,op. cit. 2..

    Google Scholar 

  14. D. Crane,op. cit. 2..

    Google Scholar 

  15. E. L. Logan, W. M. Shaw, Jr., An Investigation of the co-author graph,Journal of the American Society for Inforamtion Science, 38 (1987) 262–268.

    Google Scholar 

  16. E. L. Logan, W. M. Shaw, Jr.. On the statistical validity of co-author partitions.Processing of the American Society for Information Science, 21 (1984) 208–211.

    Google Scholar 

  17. W. M. Shaw, Jr. Statistical disorder and the analysis of a communication graph,Journal of the american Society for Information Science, 34 (1983) 146–149.

    Google Scholar 

  18. F. Harary,Graph theory, Reading, Mass, Addison-Wesley; 1969.

    Google Scholar 

  19. R. Dubes, A. K. Jain, Clustering methodologies in exploratory data analysis, In:M. C. Yovits (Ed.) Advances in Computer, 19 (1950) 576–577.

  20. R. F. Ling, G. G. Killough, Probability tables for cluster analysis based on a theory of random graphs,Journal of the American Statistical Association, 71 (1976) 293–300.

    Google Scholar 

  21. Literature of Leishmaniasis, Fileriasis, Trypansomiasis, Leprosy, Malaria, and Schistosomiasis found in theMedline database for the years 1977–1982.

  22. Computer programsCoath andRangrf, written byW. M. Shaw Jr. now of the University of North Carolina at Chapel Hill were used for Leishmaniasis, Fileriasis, and Trypanosomiasis;Coa1, andCoardn, written by Kermit Rose of the Computing Center at Florida State University were used for Leprosy, Malaria, and Schistosomiasis.

  23. E. L. Logan, W. M. Shaw, Jr. An Investigation of regularities in coauthor graphs,Proceedings of the American Society for Information Science, 25 (1988) 66–69.

    Google Scholar 

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Logan, E.L., Shaw, W.M. A bibliometric analysis of collaboration in a medical specialty. Scientometrics 20, 417–426 (1991). https://doi.org/10.1007/BF02019762

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