Interpreting epistemic and social cultural identities of disciplines with machine learning models of metadiscourse

Proceedings of ISSI 2013 - 14th International Society of Scientometrics and Informetrics Conference

Volumn 2, Issue , 2013, Pages 2027-2030

  Demarest, Bradford ^a   Sugimoto, Cassidy R ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84897611770     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Argamon, S., Dodick, J., & Chase, P. (2008). Language use reflects scientific methodology: A corpusbased study of peer-reviewed journal articles. Scientometrics, 75(2), 203-238. doi: 10. 1007/s11192-007-1768-y.
2. Becher, T. (1987). Disciplinary discourse. Studies in Higher Education, 12(3), 261-274.
3. Borgman, C. L. (1989). Bibliometrics and Scholarly Communication Editor's Introduction. Communication Research, 16(5), 583-599. doi: 10. 1177/009365089016005002.
4. Demarest, B. & Sugimoto, C. R. (2013). Using machine learning models to interpret disciplinary styles of metadiscourse in dissertation abstracts. Poster presented at iConference 2013: Scholarship in Action, Forth Worth, TX.
5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations 11(1), 10-18.
6. Hyland, K. (2004). Disciplinary Discourses: Social Interactions in Academic Writing. Ann Arbor, MI: University of Michigan Press/ELT.
7. Hyland, K. (2005). Metadiscourse: Exploring Interaction in Writing. New York, NY: Continuum.
8. Hyland, K. (2008). Disciplinary voices: Interactions in research writing. English Text Construction, 1(1), 5-22. doi: 10. 1075/etc. 1. 1. 03hyl.
9. Platt, J. (1998). Fast training of support vector machines using sequential minimal optimization. In B. Schoelkopf, C. Burges, and A. Smola (Eds.), Advances in Kernel Methods-Support Vector Learning. Cambridge, MA: MIT Press.