, Volume 107, Issue 3, pp 1227–1248 | Cite as

Knowledge in motion: the evolution of HIV/AIDS research



Many contemporary social and public health problems do not fit neatly into the research fields typically found in universities. With this in mind, researchers and funding agencies have devoted increasing attention to projects that span multiple disciplines. However, comparatively little attention has been paid to how these projects evolve over time. This relative neglect is in part attributable to a lack of theory on the dynamic nature of such projects. In this paper, we describe how research programs can move through various states of integration including disciplinarity, multidisciplinarity, interdisciplinarity and transdisciplinarity. We link this insight to computational techniques—topic models—to explore one of the most vibrant and pressing contemporary research areas—research on HIV/AIDS. Topic models of over 9000 abstracts from two prominent journals illustrate how research on HIV/AIDS has evolved from a high to a lower level of integration. The topic models motivate a more detailed historical analysis of HIV/AIDS research and, together, they highlight the dynamic nature of knowledge production. We conclude by discussing the role of computational social science in dynamic models of interdisciplinarity.


Interdisciplinarity HIV/AIDS research Topic models Dynamic networks 


  1. adams, j., & Light, R. (2014). Mapping interdisciplinary fields: Efficiencies, gaps & redundancies in HIV/AIDS research. PLoS ONE, 9(12), e115092.CrossRefGoogle Scholar
  2. Angotti, N., Dionne, K. Y., & Gaydosh, L. (2011). An offer you can’t refuse? Provider-initiated HIV testing in antenatal clinics in rural Malawi. Health Policy and Planning, 26, 307–315.CrossRefGoogle Scholar
  3. Bayat, A. (2002). Science, medicine, and the future: Bioinformatics. British Medical Journal, 324, 1018.CrossRefGoogle Scholar
  4. Bettencourt, Luis M. A., Kaiser, D. L., Kaur, J., Castillo-Chavez, C., & Wojick, D. E. (2008). Population modeling of the emergence and development of scientific fields. Scientometrics, 75(3), 495–518.CrossRefGoogle Scholar
  5. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.MathSciNetCrossRefGoogle Scholar
  6. Blei, D. M., & McAuliffe, J. D. (2010). Supervised topic models.
  7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.MATHGoogle Scholar
  8. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.CrossRefGoogle Scholar
  9. Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351–374. doi: 10.1007/s11192-005-0255-6.CrossRefGoogle Scholar
  10. Burt, R. S. (2004). Structural holes and good ideas. American Journal Sociology, 110, 349–400.CrossRefGoogle Scholar
  11. Chang, J. (2012). lda: Collapsed Gibbs Sampling Methods for Topic Models. R: CRAN.
  12. Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in neural information processing systems, 22, 288–296.Google Scholar
  13. Choi, B. C., & Pak, A. W. (2006). Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine. Medecine Clinique et Experimentale, 29, 351–364.Google Scholar
  14. Couch, S. R. (2004). A tale of three discourses: Doing action research in a research methods class. Social Problems, 51, 146–153.CrossRefGoogle Scholar
  15. Crane, D. (1972). Invisible colleges: Diffusion of knowledge in scientific communities. Chicago: University of Chicago Press.Google Scholar
  16. Dabis, F., & Ekpini, E. R. (2002). HIV-1/AIDS and maternal and child health in Africa. The Lancet, 359(9323), 2097–2104.CrossRefGoogle Scholar
  17. DiMaggio, P., Nag, M., & Blei, D. M. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US Government arts funding. Poetics, 41(6), 570–606.CrossRefGoogle Scholar
  18. Dubrow, J. K. (2011). Sociology and American studies: A CSE study in the limites of interdisciplinarity. The American Sociologist, 42, 303–315.CrossRefGoogle Scholar
  19. Epstein, S. (1995). The construction of lay expertise: AIDS activism and the forging of credibility in the reform of clinical trials. Science, Technology and Human Values, 20, 408–437.CrossRefGoogle Scholar
  20. Epstein, S. (1996). Impure science: AIDS, activism, and the politics of knowledge. Berkeley: Univ of California Press.Google Scholar
  21. Frickel, S., Albert, M., & Prainsack, B. (Eds.) (2016). Investigating interdisciplinary research: Theory and practice across the disciplines. Rutgers University Press.Google Scholar
  22. Fujimura, J. H., & Chou, D. Y. (1994). Dissent in science: Styles of scientific practice and the controversy over the cause of AIDS. Social Science and Medicine, 38(8), 1017–1036.CrossRefGoogle Scholar
  23. Gieryn, T. F. (1983). Boundary-work and the demarcation of science from non-science: Strains and interests in professional ideologies of scientists. American Sociological Review, 48, 781–795.CrossRefGoogle Scholar
  24. Gieryn, T. F. (1999). Cultural boundaries of science: Credibility on the line. Chicago: Chicago University Press.Google Scholar
  25. Gondal, N. (2011). The local and global structure of knowledge production in an emergent research field: An exponential random graph analysis. Social Networks, 33(1), 20–30.CrossRefGoogle Scholar
  26. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Science, 101(S1), 5228–5235.CrossRefGoogle Scholar
  27. Grun, B., & Hornik, K. (2011). Topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1–30.CrossRefGoogle Scholar
  28. Hackett, E. J., & Rhoten, D. R. (2009). The Snowbird Charrette: Integrative interdisciplinary collaboration in environmental research design. Minerva, 47(4), 407–440. doi: 10.1007/s11024-009-9136-0.CrossRefGoogle Scholar
  29. Hadorn, G.H., Biber-Klemm, S., Grossenbacher-Mansuy, W, Hoffmann-Riem, H, Joye, D., Pohl, C., Wiesmann, U., & Zemp, E. (Eds.). (2008). The emergence of transdisciplinarity as a form of research. In Handbook of Transdisciplinary Research (pp. 19–39). Netherlands: Springer.Google Scholar
  30. Hankins, C. (2000). Preventing mother-to-child transmission of HIV in developing countries: Recent developments and ethical implications. Reproductive Health Matters, 8(15), 87–92.CrossRefGoogle Scholar
  31. Harden, V. A. (2012). Aids at 30: A history. Washington, DC: Potomac Books.Google Scholar
  32. ISI, Thompson Reuters. (2012). Journal Citation Reports ®, Science edition.Google Scholar
  33. Jacobs, J. A. (2013). In defense of disciplines: Interdisciplinarity and specialization in the research university. Chicago: University of Chicago Press.Google Scholar
  34. Jacobs, J., & Frickel, S. (2009). Interdisciplinarity: A critical assessment. Annual Review of Sociology, 35, 43–65.CrossRefGoogle Scholar
  35. Klein, J. T. (2001). “The discourse of transdisciplinarity: An expanding global field. In J. T. Klein, W. Grossenbacher-Mansuy, R. Haberli, A. Bill, R. W. Scholz, & M. Welti (Eds.), Transdisciplinarity: Joint problem solving among science, technology and society. Basel: Birkhauser.CrossRefGoogle Scholar
  36. Klein, J. T. (2010). A taxonomy of interdisciplinarity. In R. Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford handbook of interdisciplinarity. New York, NY: Oxford University Press.Google Scholar
  37. Knorr-Cetina, K. (1999). Epistemic cultures: How the sciences make knowledge. Cambridge, MA: Harvard University Press.Google Scholar
  38. Leahey, E., & Moody, J. (2014). Sociological innovation through subfield integration. Social Currents, 1, 228–256.Google Scholar
  39. Leydesdorff, L., & Hellsten, I. (2006). Measuring the meaning of words in contexts: An automated analysis of controversies about ‘Monarch butterflies’, ‘Frankenfoods’, and ‘stem cells’. Scientometrics, 67(2), 231–258.CrossRefGoogle Scholar
  40. Leydesdorff, L., & Schank, T. (2008). Dynamic animations of journal maps: Indicators of structural changes and interdisciplinary developments. Journal of the American Society for Information Science and Technology, 59(11), 1810–1818.CrossRefGoogle Scholar
  41. Light, R. (2014). From words to networks and back: Digital tex, computational social science and the case of presidential inaugural addresses. Social Currents, 1(2), 111–129.Google Scholar
  42. Lucio-Arias, D., & Leydesdorff, L. (2009). The dynamics of exchanges and references among scientific texts, and the autopoiesis of discursive knowledge. Journal of Informetrics, 3(3), 261–271.CrossRefGoogle Scholar
  43. Marshall, E. A. (2013). Defining population problems: Using topic models for cross-national comparison of disciplinary development. Poetics, 41(6), 701–724.CrossRefGoogle Scholar
  44. McFarland, D. A., Ramage, D., Chuang, J., Heer, J., Manning, C. D., & Jurafsky, D. (2013). Differentiating language usage through topic models. Poetics, 41(6), 607–625.CrossRefGoogle Scholar
  45. McKenna, S. L., Muyinda, G. K., Roth, D., et al. (1997). Rapid HIV testing and counseling for voluntary testing centers in Africa. AIDS, 11(Suppl 1), S103–S110.Google Scholar
  46. Mohr, J. W., & Bogdanov, P. (2013). Introduction—topic models: What they are and why they matter. Poetics, 41(6), 545–569. doi: 10.1016/j.poetic.2013.10.001.CrossRefGoogle Scholar
  47. Moody, J., & Light, R. (2006). A view from above: The evolving sociological landscape. The American Sociologist, 37, 67–86.CrossRefGoogle Scholar
  48. NAS, National Academy of Sciences, National Academy of Engineering, & Institute of Medicine and Committee on Facilitating Interdisciplinary Research. (2005). Facilitating interdisciplinary research. Washington, DC: National Academies Press.Google Scholar
  49. Newman, E. A., Guest, A. B., Helvie, M. A., Roubidoux, M. A., Chang, A. E., Kleer, C. G., & Diehl, K. M. (2006). Changes in surgical management resulting from case review at a breast cancer multidisciplinary tumor board. Cancer, 107, 2346–2351.CrossRefGoogle Scholar
  50. Nicolescu, B. (2002). Manifesto of transdisciplinarity. Albany, NY: State University of New York Press.Google Scholar
  51. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.CrossRefGoogle Scholar
  52. Porter A. L., Cohen A. S., Roessner J. D., Perreault M. (2007). Measureing researcher interdisciplinarity. Scientometrics, 72, 117–147.CrossRefGoogle Scholar
  53. Powell, W. W., White, D. R., Koput, K. W., & Owen-Smith, J. (2005). Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology, 110(4), 1132–1205.CrossRefGoogle Scholar
  54. Ramage, D., Rosen, E., Chuang, J., Manning, C. D., McFarland, D.A. (2009). Topic modeling for the social sciences. in Workshop on Applications for Topic Models, NIPS. Google Scholar
  55. Salter, L., & Hearn, Alison M. V. (1997). Outside the lines: Issues in interdisciplinary research. Kingston, Ontario: Queens Univ School of Policy.Google Scholar
  56. Small, H. (2010). Maps of science as interdisciplinary discourse: Co-citation contexts and the role of analogy. Scientometrics, 83(3), 835–849.MathSciNetCrossRefGoogle Scholar
  57. Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. Handbook of Latent Semantic Analysis, 427(7), 424–440.Google Scholar
  58. Westin, T., & Stalfors, J. (2008). Tumour boards/multidisciplinary head and neck cancer meetings: Are they of value to patients, treating staff or a political additional drain on healthcare resources? Current opinion in otolaryngology & head and neck surgery, 16, 103–107.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.Department of SociologyUniversity of OregonEugeneUSA
  2. 2.University of Colorado DenverDenverUSA

Personalised recommendations