Xác định cấu trúc lĩnh vực tâm lý học: Xu hướng trong các chủ đề nghiên cứu 1995–2015

Anderson, A., McFarland, D., & Jurafsky, D. (2012). Towards a computational history of the ACL: 1980-2008. In Proceedings of the ACL-2012 special workshop on rediscovering 50 years of discoveries, association for computational linguistics (pp. 13–21). Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., et al. (2013). A practical algorithm for topic modeling with provable guarantees. In International conference on machine learning (pp. 280–288). Bail, C. A. (2014). The cultural environment: Measuring culture with big data. Theory and Society, 43(3–4), 465–482. Benjafield, J. G. (2019). Keyword frequencies in anglophone psychology. Scientometrics, 118(3), 1051–1064. Benjafield, J. G. (2020). Vocabulary sharing among subjects belonging to the hierarchy of sciences. Scientometrics, 125, 1965–1982. https://doi.org/10.1007/s11192-020-03671-7. Benoit, K., Muhr, D., & Watanabe, K. (2020). stopwords: Multilingual Stopword Lists. https://CRAN.R-project.org/package=stopwords, r package version 2.0. Berman, M. G., Jonides, J., & Nee, D. E. (2006). Studying mind and brain with fMRI. Social Cognitive and Affective Neuroscience, 1(2), 158–161. Billhardt, H., Borrajo, D., & Maojo, V. (2002). A context vector model for information retrieval. Journal of the American Society for Information Science and Technology, 53(3), 236–249. Bischof, J. M., & Airoldi, E. M. (2012). Summarizing topical content with word frequency and exclusivity. In Proceedings of the 29th International ConferEnce on Machine Learning (pp. 8–16). https://icml.cc/Conferences/2012/papers/113.pdf. Bittermann, A., & Fischer, A. (2018). How to identify hot topics in psychology using topic modeling. Zeitschrift für Psychologie, 226, 3–13. Blaheta, D., & Johnson, M. (2001). Unsupervised learning of multi-word verbs. In Proceedings of the 39th annual meeting of the ACL, association for computer linguistics (pp. 54–60). Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. Brennan, J. F., & Houde, K. A. (2017). History and systems of psychology. Cambridge: Cambridge University Press. Buurma, R. S. (2015). The fictionality of topic modeling: Machine reading Anthony Trollope’s Barsetshire series. Big Data & Society. https://doi.org/10.1177/2053951715610591. Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems (pp. 288–296). Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12(11), 671–684. DiMaggio, P., Nag, M., & Blei, D. (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. Fairburn, C. G., & Patel, V. (2017). The impact of digital technology on psychological treatments and their dissemination. Behaviour Research and Therapy, 88, 19–25. Farrell, J. (2016). Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences, 113(1), 92–97. Flis, I., & van Eck, N. J. (2018). Framing psychology as a discipline (1950–1999): A large-scale term co-occurrence analysis of scientific literature in psychology. History of Psychology, 21(4), 334–362. Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), 875–908. Gaj, N. (2016). Unity and fragmentation in psychology: The philosophical and methodological roots of the Discipline. Milton Park: Routledge. Gentner, D. (2010). Psychology in cognitive science: 1978–2038. Topics in Cognitive Science, 2(3), 328–344. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228–5235. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. Henriques, G. (2017). Achieving a unified clinical science requires a meta-theoretical solution: Comment on Melchert (2016). American Psychologist, 72(4), 393–394. Jackson, M. R. (2017). Unified clinical science, or paradigm diversity? Comment on Melchert (2016). American Psychologist, 72(4), 395–396. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. Joseph, S. (2017). The problem of choosing between irreconcilable theoretical orientations: Comment on Melchert (2016). American Psychologist, 72(4), 397–398. Kaplan, A. (2015). Opinion: Paradigms, methods, and the (as yet) failed striving for methodological diversity in educational psychology publifshed research. Frontiers in Psychology, 6, 1370. https://doi.org/10.3389/fpsyg.2015.01370. Krampen, G. (2016). Scientometric trend analyses of publications on the history of psychology: Is psychology becoming an unhistorical science? Scientometrics, 106(3), 1217–1238. Krampen, G., & Trierweiler, L. I. (2016). Some unobtrusive indicators of psychology’s shift from the humanities and social sciences to the natural sciences. International Journal of Humanities and Social Sciences, 8(3), 44–66. Krampen, G., Von Eye, A., & Schui, G. (2011). Forecasting trends of development of psychology from a bibliometric perspective. Scientometrics, 87(3), 687–694. Kwiek, M. (2020). The prestige economy of higher education journals: a quantitative approach. Higher Education. https://doi.org/10.1007/s10734-020-00553-y. Leahey, E., & Moody, J. (2014). Sociological innovation through subfield integration. Social Currents, 1(3), 228–256. Lindahl, J., Stenling, A., Lindwall, M., & Colliander, C. (2015). Trends and knowledge base in sport and exercise psychology research: a bibliometric review study. International Review of Sport and Exercise Psychology, 8(1), 71–94. Marshall, P. J. (2009). Relating psychology and neuroscience: Taking up the challenges. Perspectives on Psychological Science, 4(2), 113–125. 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. McFarland, D. A., Lewis, K., & Goldberg, A. (2016). Sociology in the era of big data: The ascent of forensic social science. The American Sociologist, 47(1), 12–35. Melchert, T. P. (2016). Leaving behind our preparadigmatic past: Professional psychology as a unified clinical science. American Psychologist, 71(6), 486–496. Miller, G. A. (2010). Mistreating psychology in the decades of the brain. Perspectives on Psychological Science, 5(6), 716–743. Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics (pp. 262–272). Morf, M. E. (2018). Agencyc Chance, and the scientific status of psychology. Integrative Psychological and Behavioral Science, 52(4), 491–507. Mullen, L. A., Benoit, K., Keyes, O., Selivanov, D., & Arnold, J. (2018). Fast, consistent tokenization of natural language text. Journal of Open Source Software, 3, 655. https://doi.org/10.21105/joss.00655. Münch, R. (2014). Academic capitalism: Universities in the global struggle for excellence. Milton Park: Routledge. Munoz-Najar Galvez, S., Heiberger, R., & McFarland, D. (2020). Paradigm wars revisited: A cartography of graduate research in the field of education (1980–2010). American Educational Research Journal, 57(2), 612–652. Preckel, F., & Krampen, G. (2016). Entwicklung und Schwerpunkte in der psychologischen Hochbegabungsforschung. Psychologische Rundschau, 67, 1–14. Rinker, T. W. (2018). Textstem: Tools for stemming and lemmatizing text. http://github.com/trinker/textstem, version 0.1.4. Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., et al. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082. Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A model of text for experimentation in the social sciences. Journal of the American Statistical Association, 111(515), 988–1003. Rule, A., Cointet, J.-P., & Bearman, P. S. (2015). Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790–2014. Proceedings of the National Academy of Sciences, 112(35), 10837–10844. Schwartz, S. J., Lilienfeld, S. O., Meca, A., & Sauvigné, K. C. (2016). The role of neuroscience within psychology: A call for inclusiveness over exclusiveness. American Psychologist, 71(1), 52–70. Shi, F., Foster, J. G., & Evans, J. A. (2015). Weaving the fabric of science: Dynamic network models of science’s unfolding structure. Social Networks, 43, 73–85. Toomela, A. (2019). The Problem Psychology: A Science Yet to Become a Science. In A. Toomela (Ed.), The Psychology of Scientific Inquiry (pp. 1–11). Cham: Springer. Tryon, W. W. (2017). Basing clinical practice on unified psychological science: Comment on Melchert (2016). The American Psychologist, 72, 399–400. Yeung, A. W. K. (2018). Bibliometric study on functional magnetic resonance imaging literature (1995–2017) concerning chemosensory perception. Chemosensory Perception, 11(1), 42–50. Yeung, A. W. K., & Ho, Y.-S. (2018). Identification and analysis of classic articles and sleeping beauties in neurosciences. Current Science, 114(10), 2039–2044. Yeung, A. W. K., Goto, T. K., & Leung, W. K. (2017a). At the Leading Front of Neuroscience: A Bibliometric Study of the 100 Most-Cited Articles. Frontiers in Human Neuroscience, 11, 363. https://doi.org/10.3389/fnhum.2017.00363. Yeung, A. W. K., Goto, T. K., & Leung, W. K. (2017b). The Changing Landscape of Neuroscience Research, 2006–2015: A Bibliometric Study. Frontiers in Neuroscience, 11, 120. https://doi.org/10.3389/fnins.2017.00120. Zagaria, A., Ando’, A., & Zennaro, A. (2020). Psychology: A giant with feet of clay. Integrative Psychological and Behavioral Science, 54(3), 1–42.