Skip to main content
SpringerLink
Log in

Knowledge and structures of scientific growth measurement of a cancer problem domain

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

In the context of bridging the so-called externalist and cognitive perspectives on the growth of research communities, a cancer “problem domain” is examined (1) to distinguish a growth in knowledge from a proliferating research literature, and (2) show how measurement of formal communication, uninformed by the “historical record,” clarifies or distorts sociological interpretations of innovation and growth in biomedicine. Specifically, coauthorship and citation networks are analyzed for reverse transcriptase researchers, 1970–74. This analysis reveals the visibility of large National Cancer Institute laboratories in the research literature, but demonstrates the need to augment disaggregated network data with intellectual and social (policy) history to explain the growth and structure of the domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes and references

  1. We conceptualize “problem domains” as cognitive regions around which scientists gather (intellectually) and through which they pass (as evidenced by publication on problems specific to the region). These domains, we think, tend to be shorter-lived and encompass fewer researchers than so-called scientific specialties [for a review, see D. E. CHUBIN, The conceptualization of scientific specialties,Sociological Quarterly, 17 (Autumn/1976), 448–476] The best approximations in the sociology of science literature of “problem domain” are the concepts of “research area” (R. D. WHITLEY, Cognitive and social institutionalization of scientific specialties and research areas, in:Social Progresses of Scientific Development, R. D. WHITLEY (Ed.), London, Routledge and Kegan Paul, 1974, p. 65–95) and “transient network” (D. O. EDGE, M. J. MULKAY,Astronomy Transformed, New York, Wiley, 1976, esp. Chapt. 10). Network, unfortunately, connotes social properties which are construed all too readily as criteria for identifying a viable domain. We do not equate network with domain; reather, we see a domain as composed of networks through which ideas flow, often linked by clusters of researchers at certain institutional sites. But above all, a problem domain is an intellectual entity, a nexus of research interests; we assume nothing about the social structure of those interests.

    Article  Google Scholar 

  2. These terms include what others have referred to as “cognitive and technical norms” [M. J. MULKAY, Some aspects of cultural growth in the natural sciences,Social Research, 36 (Spring 1969) 22–52]; “tacit knowledge” [M. POLANYI,The Tacit Dimension, Garden City, N. Y.: Anchor Doubleday, 1967; H. M. COLLINS, The TEA set: tacit knowledge and scientific networks,Science Studies, 4 (1974) 165–186; and the “inner logic of a set of scientific problems” (G. BOHME, Models for the development of science, inScience, Technology and Society: A Cross-Disciplinary Perspective, I. SPIEGEL-RÖSING, D. DE S. PRICE (Eds), Beverly Hills, Sage, 1977, p. 319–352].

    Google Scholar 

  3. J. R. COLE, H. ZUCKERMAN, The emergence of a scientific specialty: the self-exemplifying case of the sociology of science, inThe Idea of Social, Structure: Pepers in Honor of Robert K. Merton, L. A. COSER (Ed.), p. 139–174, New York, Harcourt Brace, 1975, B. C. GRIFFITH, M. G. DROTT, H. G. SMALL, On the use of citations in studying scientific achievements and communication,Newsletter of the Society for Social Studies of Science, 2 (Summer 1977) 9–13; D. O. EDGE, Why I am not a co-citationist,Newsletter of the Society for Social Studies of Science, 2 (Summer 1977) 13–19.

    Google Scholar 

  4. H. G. SMALL, B. C. GRIFFITH, The structure of scientific literatures I: Identifying and graphing specialties,Science Studies, 4 (1974) 17–40; N. C. MULLINS, L. L. HARGENS, P. K. HECHT, E. L. KICK, The group structure of cocitation clusters: a comparative study,Americal Sociological Review, 42 (August 1977) 552–562.

    Google Scholar 

  5. M. D. KING, Reason, tradition, and the progressiveness of science,History and Theory, 10 (1971) 3–32.

    Google Scholar 

  6. S. S. BLUME, Sociology of sciences and sociologies of science, inPerspectives in the Sociology of Science, S. S. BLUME (Ed.), Chichester, Wiley, 1977, p. 13.

    Google Scholar 

  7. Ibid., p. 12.

    Google Scholar 

  8. For related statements that warrant careful examination, see R. JOHNSTON, Contextual knowledge: A model for the overthrow of the internal/external dichotomy in science,Australian and New Zealand Journal of Science, 12 (October 1976) 193–203, R. MACLEOD, Changing perspectives in the social history of science, inScience, Technology and Society: A Cross-Disciplinary Perspective, I. SPIEGEL-RÖSING, D. de S. PRICE (Eds), Beverly Hills, Sage, 1977, p. 149–195; E. MENDELSOHN, The social construction of scientific knowledge, inThe Social Production of Scientific Knowledge, E. MENDELSOHN, P. WEINGART, and R. WHITLEY (Eds.), Boston, Reidel, 1977, p. 3–26; R. KROHN, Scientific ideology and scientific process: The natural history of a conceptual shift, inThe Social Production of Scientific Knowledge, E. MENDELSOHN et al. (Eds), Boston, Reidel, 1977, p. 69–99.

    Google Scholar 

  9. R. WHITLEY, Black boxism and the sociology of science: a discussion of major developments in the field,The Sociological Review Monograph, 18 (September 1972) 61–92.

    Google Scholar 

  10. I. SPEIGEL-RÖSING, The study of science, technology and society (SSTS): Recent trends and future challenges, inScience..., I. SPIEGEL-RÖSING, D. de S. PRICE (Eds), 1977, p. 24, 31.

  11. D. de S. PRICE,Little Science, Big Science, New York, Columbia University Press, 1963, esp. Chapter 3; W. O. HAGSTROM,The Scientific Community, New York, Basic Books, 1965. N. C. MULLINS,Theory and Theory Groups in Contemporary American Sociology, New York, Harper and Row, 1973; D. CRANE,Invisible Colleges, Chicago, University of Chicago Press, 1972; J. GASTON,Originality and Competition in Science, Chicago, University of Chicago Press. Also see notes 1 and 2.

    Google Scholar 

  12. See M. J. MULKAY, Sociology of the scientific research community, inScience..., I. SPIEGEL-RÖSING, D. de S. PRICE (Eds), 1977, p. 125f.; W. VANDENDAELE, W. KROHN, P. WEINGART, The political direction of scientific development, inThe Social Production of Scientific Knowledge, E. MENDELSOHN et al. (Eds), Boston, Reidel, 1977, p. 219–242.

  13. For a history of this campaign, see S. STRICKLAND,Politics, Science, and Dread Disease, Cambridge, Mass., Harvard University Press, 1972.

    Google Scholar 

  14. See. L. D. LONGO, Some problems facing biomedical research,Federation Proceedings, 32 (November 1973) 2078–2085; T. GUSTAFSON, The controversy over peer review,Science, 190 (12 December 1975) 1060–1066; J. HIXSON,The Patchwork Mouse, Garden City, N. J., Anchor Doubleday, 1976; R. RETTIG,Cancer Crusade: The Story of the National Cancer Act of 1971, Princeton; Princeton University Press, 1977; D. E. CHUBIN K. E. STUDER, The politics of cancer,Theory and Society, 6 (July 1978) 55–74.

    Google Scholar 

  15. As S. W. WOOLGAR [The identification and definition of scientific collectivities, inNow Perspectives in the Emergence of Scientific Disciplines, G. LEMAINE et al. (Eds), Chicago, Aldine, 1976, p. 233–245] has demonstrated, research communities or “collectivities” have no inherent boundaries; they are moredefined than identified through a somewhat arbitrary circumscription of a subject and selection of a literature that represents it. Our prior research [D. E. CHUBIN, E. STUDER, Search and research on scientific specialties: the case of viral cell transformation, Working paper, 1976; K. E. STUDER, D. E. CHUBIN, Biological ‘problem domains’: the case of cell transformation to mid-twentieth century and its theoretical implications, Cornell University, SASS Working Paper, 1975] assured us that categorization of the biomedical literature is particularly difficult (see note 17 below). The convergence of the enzymologist, virologist, cytologist, biochemist, and immunologist onto a problem seemingly occurs because researching the problem demands a confluence of these various perspectives.

    Google Scholar 

  16. H. M. TEMIN, D. BALTIMORE, in a 1972 review article (RNA-directed DNA synthesis,Advances in Virus Research, 17, 129–186), state that immediately after the discovery of DNA polymerases in virions of RNA viruses, a great deal of hope was expressed that this discovery would lead to resolving the question of the involvement of RNA tumor viruses in an inapparent form in ‘spontaneous’ or chemical carcinogen-induced tumors, especially in man. So far there has been remarkably little evidence supporting the hypothesis that viruses like the animal RNA tumor viruses are related to human neoplasia (p. 172). TEMIN, in concluding his Nobel lecture three years later (The DNA provirus hypothesis,Science, 192 (11 June 1976) 1075–1080), reiterates this point: ...I do not believe that infectious viruses cause most human cancers, but I do believe that viruses provide models of the processes involved in the etiology of human cancer (p. 1080).

  17. The three sources used to compile RT articles wereIndex Medicus (MEDLARS),Biological Abstracts, and theSource Index of theScience Citation Index. This set totals 656 articles. In large measure our definition of the RT literature was placed in the hands of those indexing and assigning keywords to articles. Because 80 per cent of our article set was retrieved fromIndex Medicus (22 percent of these articles were also found in one or both of the other sources), we appealed to a classification system used by biologists themselves. The constant revision of this hierarchical keyword system attests to shifts in categories prompted by advances in biomedical research. At the same time we express confidence in defining a large segment of the RT set according to such a “cognitive” system, we acknowledge the difficulties in utilizing such a system and analyzing keywords as a content variable. We are also resigned to operating on a purposive sample of RT articles from a statistically “unknown” population. However, this should not be treated as either artifact or flaw [M. S. GRANOVETTER, Network Sampling: some first steps,American Journal of Sociology, 81 (1976) 1287–1303], but as a realistic appraisal of the subject matter — and all the more reason to adopt a multiple measurement strategy or “triangulate” on it.

    Article  Google Scholar 

  18. The historical record includes primary source articles, research reports, and conference proceedings as well as review articles, summaries in the scientific press, and first-person retrospectives such as Nobel addresses, festschrifts, and one important source not considered here, interviews. On the importance of interviews in science studies, see M. J. MULKAY, Methodology in the sociology of science: some reflections on the study of radio astronomy,Social Science Information, 13 (1974) 107–119; H. ZUCKERMAN, Interviewing an ultra-elite, Appendix A inScientific Elite: Nobel Laureates in the United States, New York, Free Press, 1977, p. 255–279, S. W. WOOLGAR, Writing an intellectual history of scientific development: The use of discovery accounts,Social Studies of Science, 6 (September 1976) 395–422; R. OLBY,The Path to the Double Helix, Seattle, University of Washington Press 1974.

    Google Scholar 

  19. J. D. WATSON,Molecular Biology of the Gene, 2nd ed., New York, W. A. Benjamin, 1970, p. 331.

    Google Scholar 

  20. D. E. CHUBIN, The journal as a primary data source in the sociology of science: with some observations from sociology,Social Sciences Information, 14 (1975) 157–168; MULLINS, op. cit., note 11Theory and Theory Groups in Contemporary American Sociology, New York, Harper and Row, 1973, esp. Methodological Appendix.

    Google Scholar 

  21. For discussion of informal communication; e.g., “trusted assessorship” patterns, see D. E. CHUBIN, Trusted assessorship in science: a relation in need of data,Social Studies of Science, 5 (August 1975) 362–368; P. K. WOOLF, The second messenger: informal communication in cyclic AMP research.Minerva, 14 (1975) 349–373. For an analysis of keyword configurations, see K. E. STUDER,Growth and Specialization in Contemporary Biomedicine: The Case of Reverse Transcriptase, Cornell University, Unpublished doctoral dissertation, 1977. esp. Chapter 7.

    Google Scholar 

  22. For classic statements, see E. BOTT,Family and Social Network, London, Tavistock, 1957; S. F. NADEL,The Theory of Social Structure, London, Cohen and West, 1957.

    Google Scholar 

  23. See note 4; H. G. SMALL, Co-citation in the scientific literature: A new measure of the relationship between two documents.Journal of American Sociology Information Science, 24 (1973) 265–169; H. G. SMALL, Multiple citation patterns in scientific literature: the circle and hill models,Information Storage and Retrieval, 10 (1974) 393–402; B. C. GRIFFITH, H. G. SMALL, J. A. STONEHILL, D. DEY, The structure of scientific literatures II: toward a macro- and microstructure for science,Science Studies, 4 (1974) 339–365.

    Google Scholar 

  24. For example, see R. D. ALBA, M. D. GUTMANN, SOCK: a sociometric analysis system,Behavioral Science, 17 (May 1972) 326. The crux of multidimensional scaling is to capture in a geometrical representation as much information as possible in the fewest number of dimensions. The goodness of fit information in n dimensions is called “stress’ (whose minimum value is zero). In all of the scaled figures that follow, the stress values are sufficiently low (<0.37) that two dimensional representation is more than adequate.

    Google Scholar 

  25. D. de S. PRICE, D. De B. BEAVER, Collaboration in an invisible college,American Psychologist, 2 (November 1966) 1011–1018.

    Google Scholar 

  26. Twelve percent of the article set (77/656) is accounted for by the co-authorships among these 16 researchers.

  27. H. ZUCKERMAN, op. cit., note 18, 228.

    Google Scholar 

  28. This is a selective bias which is tantamount to excluding so-called peripheral persons with “weak ties” to a network [M. S. GRANOVETTER, The strength of weak ties,American Journal of Sociology, 78 (May 1973) 1360–1380]. This observation also harbors methodological implications for understanding sources of scientific innovation in a domain and convergences of the innovators. See CHUBIN, op. cit., note 1 The conceptualization of scientific specialties,Sociological Quarterly, 17 (Autumn 1976), 448–476.; and M. J. MULKAY, Conceptual displacement and migration in science: A prefatory paper,Science Studies, 4 (1974) 205–234.

    Article  Google Scholar 

  29. According to TEMIN, 1077–1078), “an even more satisfying proof for the existence of the DNA provirus was the demonstration, first by HILL and HILLOVA [in 1972], of infectious DNA for RSV [Rous sarcoma virus].”

    Google Scholar 

  30. S. SPIEGELMAN, A. BURNY, M. R. DAS, J. KEYDAR, J. SCHOLM, M. TRAVNICEK, K. WATSON, Characterization of the products of RNA-directed DNA polymerases in oncogenic RNA viruses,Nature, 227 (8 August 1970) 563–567.

    Article  Google Scholar 

  31. The numbers of authors and articles, respectively, encompassed by the major networks are 50 and 113 in 1973. To grasp the degree of data reduction these numbers represent, in 1973, out of 215 articles, 173 (80 percent) were co-authored by 460 different people who comprise 71 disjoint subsets, only five of which published four or more articles within the year.

  32. Our conjectures require other structural data (e.g., citation analyses which are presented below), but moreover, lack corroboration by otherkinds of data. Therefore, we are presently visiting several of the key NCI and academic labs and are interviewing some of the directors and team members involved in post-RT discovery events and publications.

  33. In all fairness, one might also speculate that the medical orientation which seems to prevail at some NCI labs would favor immunological and epidemiological investigations of oncogenic viruses, instead of more basic genetic and virological experimentation which led TEMIN and BALTIMORE (who are Ph. D.'s, not M. D.'s, as are often found at NCI) to the discovery of RNA-directed DNA synthesis.

  34. A. M. WEINBERG, The coming age of biomedical science,Minerva, 4 (1965) 3–14.

    Article  Google Scholar 

  35. See D. E. CUBIN, S. MOITRA, Content analysis of references: adjunct of alternative to citation counting?Social Studies of Science, 5 (November 1975) 423–441; N. KAPLAN, The norms of citation behavior: prolegomena to the footnote,American Documentation, 16 (July 1965) 179–184.

    Google Scholar 

  36. For representative illustrations, see J. R. COLE, S. COLE,Social Stratification in Science, Chicago, University of Chicago Press, 1973; D. de S. PRICE, Citation measures of hard science, soft science, technology and nonscience, inCommunication Among Scientists and Engineers, C. E. NELSON, D. K. POLLOCK (Eds.), Lexington, Mass., D. C. Health, 1970, p. 3–22; D. de S. PRICE, S. GURSEY, Studies in scientometrics. Part I. Transience and continuance in scientific authorship,International Forum on Information and Documentation, 1 (1976) 17–24; D. de S. PRICE, S. GURSEY, Studies in scientometrics. Part III. The relation between source author and cited author populations,International Forum on Information Science and Documentation, 1 (1976) 19–22.

    Google Scholar 

  37. The correlation between the number of references given by and citations retrieved from the 16 co-authors linked in the in-house network is r=0.864.

  38. Like factor analysis, eigenstructure analysis is based upon solving for the eigenvalues and vectors (also called characteristic or latent roots and vectors) of a matrix (see W. W. COOLEY, R. LOHNES,Multivariate Data Analysis, New York, Wiley, 1971; P. E. GREEN, Mathematical Tools for ‘Applied’ Multivariate Analysis, New York, Academic Press, 1976). Because this method is the same as that employed in multidimensional scaling routines, an eignestructure analysis will provide a means of decomposing the visual network relationships displayed in the scaled figures. Because network matrices are structural matrices, the analysis must therefore be interpreted in terms of the “structure-maximizing” properties of eigenvalues and eigenvectors; the vectors associated with the highest eigenvalues isolate the structurally most central features of a network system. For further details, see STUDER, op. cit., note 21Growth and Specialization in Contemporary Biomedicine: The Case of Reverse Transcriptase, Cornell University, Unpublished doctoral dissertation, 1977. esp. Chapter 6 and Avant-Propos.

    Google Scholar 

  39. There is no rigorous justification for trying to interpret the weighted structural properties of any but the principal eigenvector of the co-citation network.

  40. A complete list of the 66 articles which meet the citation and co-citation criteria is avialable upon request from the authors.

  41. M. ROKUTANDA, H. ROKUTANDA, M. GREEN, K. FIJINAGA, R. K. RAY, C. GURGO, Formation of viral RNA-DNA polymerase of sarcoma-leukaemia viruses,Nature, 227 (5 September 1970) 1026.

    Google Scholar 

  42. STUDER, op. cit., note 21.Growth and Specialization in Contemporary Biomedicine: The Case of Reverse Transcriptase, Cornell University, Unpublished doctoral dissertation, 1977. esp. Chapter 7. Actually, GREEN based his prediction of a protein capsidin the virus on the then-unpublished data of NCI microbiologist J. BADER whose inhibitor researcher paralleled that of TEMIN since 1965, earning BADER a polite footnote in TEMIN's Nobel address.

  43. STUDER, op. cit., note 21Growth and Specialization in Contemporary Biomedicine: The Case of Reverse Transcriptase, Cornell University, Unpublished doctoral dissertation, 1977. esp. Chapter 7, 286.

  44. O. H. LOWRY, N. J. ROSENBROUGH, A. L. FARR, R. J. RANDALL, Protein measurement with the folin phenol reagent,Journal of Biological Chemistry, 193 (1951) 265–275. This paper has accumulated more citations than any other paper ever published, as measured by theScience Citation Index.

    PubMed  Google Scholar 

  45. STUDER, op. cit., note 21Growth and Specialization in Contemporary Biomedicine: The Case of Reverse Transcriptase, Cornell University, Unpublished doctoral dissertation, 1977. esp. Chapter 7, 316–317.

  46. See M. J. MULKAY, Norms and ideology in science,Social Science Information, 15 (1976) 637–656.

    Google Scholar 

  47. This appears to be contrary to MULLINS (op. cit., note 11) who searches for program statements as a necessary condition for scientific growth. Part of our ongoing research on RT is to study the function of such statements, but not to assume that the outworking of the domain can be reduced from them.

    Google Scholar 

  48. Nevertheless, in RT, money (in the form of generous contract research funds dispensed by NCI's Special Virus Cancer Program) begat research (predicated on a viral etiology of cancer) which begat articles, which were ultimately cited — all of which increased (1) the visibility of NCI, (2) its negotiating position for continued funding vis-a-vis Congress, and (3) the expectations of “curing cancer in your lifetime.” Unless such a cycle is far-fetched, it should not be surprising that certain intramural NCI research and that produced by major extramural contractors would be highly cited — politicized, if you will, to help legitimate the mission and signify “progress.” See CHUBIN and STUDER, op. cit., note 14.

    Article  Google Scholar 

  49. A. J. MEADOWS, J. G. O'CONNOR [Bibliographic statistics as a guide to growth points in science,Science Studies, 1 (January 1971) 95–99], in their research on pulsar literature, lend support to our argument. They argue that “... in the initial stages of a new growth area only a few scientific groups are likely to participate in the research. Hence, to begin with, the literature available for citing by any group will be to a significant extent that group's own work. As time passes and more scientists are attracted into the new area, the proportion of the literature due to any given group of authors must decrease. Thus, the self-citation rate should be above average at first, decreasing gradually to the normal level for the field” (p. 97). But if such is the case, then co-citations can not be taken at face value as indicative of the intellectual state of the field, specialty or problem domain. Co-citation clusters may simply isolate the early institutional contexts of scientific development, i.e., the most “coherent groups” [D. C. GRIFFITH, N. C. MULLINS, Coherent social groups in scientific change,Science, 177 (15 September 1972) 959–964], and later, the most visible “invisible colleges.”

    Google Scholar 

  50. This has been demonstrated by H. G. SMALL, A co-citation model of a scientific specialty: a longitudinal study of collagen research,Social Studies of Science, 7 (May 1977) 139–166; D. SULLIVAN, D. H. WHITE, E. J. BARBONI, Co-citation analyses of science: an evaluation,Social Studies of Science, 7 (May 1977) 223–240.

    Google Scholar 

  51. The attraction of researchers to “hot” or “fashionable” problem domains (or surrounding regions) is of obvious interest to science policy-makers. Do scientists go where the money is? The problem can be studied by reconstructing how “flurries of activity” evolve into more sustained concentrations of ideas and people, or dissipate altogether. The findings would indeed be suggestive: if domains are formed by confluences of ideas and personnel, then could “migration streams” or “chain migration” be artificially induced (e.g., through an earmarking of training funds in an area) to accelerate discoveries or innovations? This would be an example of a “trickle up” effect in which findings derived from microscopic analyses are instructive for the modification of a policy made and administered at the macroscopic, e.g., federal, level.

  52. Op. cit., note 4. Also see H. C. WHITE, S. A. BOORMAN, R. L. BREIGER, Social structure from multiple networks. I. Blockmodels of roles and positions,American Journal of Sociology, 81 (January 1976) 730–780; R. L. BREIGER, Career attributes and network structure: a blockmodel study of a biomedical research specialty,American Sociological Review, 41 (February 1976) 117–135.

    Article  Google Scholar 

  53. For a more comprehensive treatment of this point, see D. E. CHUBIN, P. T. CARROLL, K. E. STUDER, Underpinnings and overselling: a comment upon two blockmodel studies of cocitation clusters, Unpublished paper, (December 1977).

  54. See K. E. STUDER, Interpreting scientific growth: a comment on D. PRICE's ‘Science Since Babylon’.”History of Science, 15 (1977) 44–51.

    Google Scholar 

  55. Quoted in Anonymous, Tumor virology: the Paris fashion,Nature, 228 (14 November) 609–610.

Download references

Author information

Authors and Affiliations

  1. Georgia Institute of Technology, 30332, Atalanta, Georgia, (USA)

    D. E. Chubin

  2. Virginia Commonwealth University, (USA)

    K. E. Studer

Authors
  1. D. E. Chubin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. E. Studer
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chubin, D.E., Studer, K.E. Knowledge and structures of scientific growth measurement of a cancer problem domain. Scientometrics 1, 171–193 (1979). https://doi.org/10.1007/BF02016969

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02016969

Keywords

Search

Navigation