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Digging into the PIT: A new tool for characterizing the social paleontological community

, , , University of Florida, United States

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Las Vegas, NV, United States ISBN 978-1-939797-35-3 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA


The purpose of this study is to share the development of a new analytical tool, the Paleontological Identity Taxonomy (PIT), for characterizing members of a science community of practice that is sensitive to and descriptive of individual differences, but also inclusive of a broad range of people who identify with the domain, from those that are strictly interest-based to those with professional credentials. Via the use of embedded mixed methods, including aggregating social network data and iterative coding sessions, we present the PIT as a valid and reliable tool which can be used to characterize members of digital social spaces, an issue previously viewed as a limitation for use of social network analysis in social science research. We conclude with remarks concerning the design of effective scientific learning and communication for social media, including how to recognize and support the diversity of community members’ expertise.


Lundgren, L., Crippen, K.J. & Bex, II, R.T. (2018). Digging into the PIT: A new tool for characterizing the social paleontological community. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 121-128). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved March 26, 2019 from .

View References & Citations Map


  1. Alexander, P.A. (2003). The development of expertise: the journey from acclimation to proficiency. Educational Researcher, 32(8), 10–14.
  2. Burns, M., & Medvecky, F. (2018). The disengaged in science communication: How not to count audiences and publics. Public Understanding of Science (Bristol, England), 27(2), 118–130.
  3. Catalani, J. (2014). Contributions by amateur paleontologists in 21st century paleontology. Palaeontologia Electronica.
  4. Côté, I.M., & Darling, E.S. (2018). Scientists on Twitter: Preaching to the choir or singing from the rooftops? FACETS, 3(1), 682–694.
  5. Creswell, J.W. (2009). Research Design: Qualitative, Quantitative, and Mixed Method Approaches (3rd ed.). Thousand Oaks, CA: SAGE Publications, Inc.
  6. Crippen, K.J., Ellis, S., Dunckel, B.A., Hendy, A.J.W., & MacFadden, B.J. (2016). Seeking shared practice: A juxtaposition of the attributes and activities of organized fossil groups with those of professional paleontology. Journal of Science Education and Technology, 25(5), 731–746. Doi:10.1007/s10956-0169627-3
  7. Crossley, N. (2011). Networks and complexity: directions for interactionist research? Symbolic Interaction, 33(3), 341–363.
  8. Cross, R., Laseter, T., Parker, A., & Velasquez, G. (2011). Assessing and Improving Communities of Practice with Organizational Network Analysis. The Network Roundtable at the University of Virginia.
  9. Daume, S., & Galaz, V. (2016). “Anyone know what species this is?”-Twitter conversations as embryonic Citizen Science communities. Plos One, 11(3), e0151387.
  10. Gibson, J.J. (1986). The Ecological Approach to Visual Perception. Hillsdale, NJ: Lawrence Erlbaum Associates.
  11. Gruzd, A., Paulin, D., & Haythornthwaite, C. (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71.
  12. Krippendorff, K. (2012). Content Analysis: An Introduction to Its Methodology (illustrated.). SAGE.
  13. Larson, N.L., Stein, W., Triebold, M., & Winters, G. (2016). What commercial fossil dealers contribute to paleontology. Journal of Paleontological Sciences, (10).
  14. Liberatore, A., Bowkett, E., MacLeod, C.J., Spurr, E., & Longnecker, N. (2018). Social media as a platform for a citizen science community of practice. Citizen Science: Theory and Practice, 3(1).
  15. MacFadden, B.J., Lundgren, L., Crippen, K.J., Dunckel, B., & Ellis, S. (2016). Amateur paleontological societies and fossil clubs, interactions with professional paleontologists, and social paleontology in the United States. Palaeontologia Electronica, 19(2), 1E.
  16. Priem, J., & Costello, K.L. (2010). How and why scholars cite on Twitter. Proceedings of the American Society for Information Science and Technology, 47(1), 1–4.
  17. Sfard, A., & Prusak, A. (2005). Telling identities: in search of an analytic tool for investigating learning as a culturally shaped activity. Educational Researcher, 34(4), 14–22.
  18. Smith, M., Ceni, A., Milic-Frayling, N., Shneiderman, B., Mendes Rodrigues, E., Leskovec, J., & Dunne, C. (2010). NodeXL: a free and open network overview, discovery and exploration add-in for Excel 2007/2010/2013/2016, From the Social Media Research Foundation (Version
  19. Wenger, E., White, N., & Smith, J.D. (2009). Digital habitats: stewarding technology for communities (Vol. EBook). Portland, OR: CPsquare.
  20. Zhao, X., Lampe, C., & Ellison, N.B. (2016). The social media ecology: user perceptions, strategies and challenges. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems-CHI ’16 (pp. 89–

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