Using deep learning language models as scaffolding tools in interpretive research
Main Article Content
André Luis Araujo da Fonseca
Paula Castro Pires de Souza Chimenti
Maribel Carvalho Suarez
Abstract
Objective: the paper introduces a framework for conducting interpretive research using deep learning algorithms that blur the boundaries between qualitative and quantitative approaches. The work evidences how research might benefit from an integrated approach that uses computational tools to overcome traditional limitations. Proposal: the increased availability and diversity of data raises the utility of algorithms as research tools for social scientists. Furthermore, tuning and using such computational artifacts may benefit from interpretive procedures. Such circumstances turn the traditional debate between quantitative and qualitative research on its head: the research strategy that likely yields the most assertiveness and rigor is the one that may require vigorous hermeneutic effort. Along these lines, neural word embeddings can be instrumental in allowing researchers to read the data closely before and after interpretation. Conclusions: to take advantage of the opportunity generated by these new algorithms, researchers may broaden their previous conceptions and adopt a participative point of view. In the coming decades, the interweaving of computational and interpretive methods has the potential to integrate rigorous social science research.
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How to Cite
Fonseca, A. L. A. da, Chimenti, P. C. P. de S., & Suarez, M. C. (2023). Using deep learning language models as scaffolding tools in interpretive research. Journal of Contemporary Administration, 27(3), e230021. https://doi.org/10.1590/1982-7849rac2023230021.en
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Invited Article
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Campagnolo, G. M. (2021). Participative epistemology in social data science: Combining ethnography with computational and statistical approaches. International Journal of Social Research Methodology, 25(3), 1–13. https://doi.org/10.1080/13645579.2021.1892379
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Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67–80. https://doi.org/10.1016/j.dss.2013.08.008
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Creswell, J. W. (2013). The Selection of a Research Approach. In Research design: Qualitative, quantitative and mixed methods approaches (pp. 30–55). SAGE Publications.
Dados, N., & Connell, R. (2012). The Global South. Contexts, 11(1), 12–13. https://doi.org/10.1177/1536504212436479
Deleuze, G. (1968). Difference and Repetition. Columbia University Press.
Deleuze, G., & Guattari, F. (1980). A thousand plateaus: Capitalism and schizophrenia. University of Minnesota Press. https://doi.org/10.4324/9780203584200
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. http://arxiv.org/abs/1810.04805
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Evans, M. S. (2014). A computational approach to qualitative analysis in large textual datasets. PLoS ONE, 9(2). https://doi.org/10.1371/journal.pone.0087908
Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance and Investment, 5(4), 210–233. https://doi.org/10.1080/20430795.2015.1118917
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Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535–574. https://doi.org/10.1257/jel.20181020
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4). https://doi.org/10.1287/mksc.1040.0071
Goertz, G., & Mahoney, J. (2012). A tale of two cultures: Qualitative and quantitative research in the social sciences. Princeton University Press.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2021). Machine learning for Social Science: An agnostic approach. Annual Review of Political Science, 24, 395–419. https://doi.org/10.1146/annurev-polisci-053119
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the Social Sciences. Princeton University Press.
Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). “I think this is the most disruptive technology”: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. http://arxiv.org/abs/2212.05856
Hartley, S. (2017). Fuzzy and the techie - Why the liberal arts will rule the digital world. Journal of Chemical Information and Modeling, (9). https://doi.org/10.1017/CBO9781107415324.004
Heron, J., & Reason, P. (1997). A participatory inquiry paradigm. Qualitative Inquiry, 3(3), 274–294. https://doi.org/10.1177/107780049700300302
Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39, 61–72. https://preventviolentextremism.info/sites/default/files/The%20Field%20Behind%20the%20Screen-%20Using%20Netnography%20For%20Marketing%20Research%20in%20Online%20Communities.pdf
Kozinets, R. V., Scaraboto, D., & Parmentier, M. A. (2018). Evolving netnography: How brand auto-netnography, a netnographic sensibility, and more-than-human netnography can transform your research. Journal of Marketing Management, 34(3-4), 231–242. https://doi.org/10.1080/0267257X.2018.1446488
Kuehn, D., & Rohlfing, I. (2022). Do quantitative and qualitative research reflect two distinct cultures? An empirical analysis of 180 articles suggests “no.” Sociological Methods & Research. https://doi.org/10.1177/00491241221082597
Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 595(7866), 189–196. https://doi.org/10.1038/s41586-021-03660-7
Lazer, D., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., Wagner, C., & Lane, J. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1177/0002716217740116
Li, Z., Dohan, D., & Abramson, C. M. (2021). Qualitative coding in the computational era: A hybrid approach to improve reliability and reduce effort for coding ethnographic interviews. Socius, 7, 1–4. https://doi.org/10.1177/23780231211062345
Marcus, G. E. (1995). Ethnography in/of the World System: The emergence of multi-sited ethnography. Annual Review of Anthropology, 24(1995), 95–117. https://doi.org/10.1146/annurev.an.24.100195.000523
Marres, N. (2020). For a situational analytics: An interpretative methodology for the study of situations in computational settings. Big Data and Society, 7(2). https://doi.org/10.1177/2053951720949571
Matthews, M. R. (2022). Thomas Kuhn and Science Education: Learning from the past and the importance of History and Philosophy of science. Science and Education. https://doi.org/10.1007/s11191-022-00408-1
Matzner, T. (2022). Algorithms as complementary abstractions. New Media and Society. https://doi.org/10.1177/14614448221078604
Meckin, R. (2021). Interdisciplinary report Computational Social Science: A thematic review. https://eprints.ncrm.ac.uk/id/eprint/4476
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. http://arxiv.org/abs/1301.3781
Mohr, J. W., Bail, C. A. B., Frye, M., Lena, J. C., Lizardo, O., McDonnell, T. E., Mische, A., Tavory, I., & Wherry, F. F. (2020). Measuring culture. Columbia University Press.
Mohr, J. W., Wagner-Pacifici, R., & Breiger, R. L. (2015). Toward a computational hermeneutics. Big Data and Society, 2(2). https://doi.org/10.1177/2053951715613809
Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1(1), 48–76. https://doi.org/10.1177/2345678906292462
Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological Methods and Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703
Nelson, L. K., Burk, D., Knudsen, M., & McCall, L. (2018). The future of coding: A comparison of hand-coding and three types of computer-assisted text analysis methods. Sociological Methods and Research, 50(1). https://doi.org/10.1177/0049124118769114
O’Neil, C. (2016). Weapons of Math destruction: How Big Data increases inequality and threatens democracy. Crown.
Park, M., Leahey, E., & Funk, R. J. (2023). Papers and patents are becoming less disruptive over time. Nature, 613(7942), 138–144. https://doi.org/10.1038/s41586-022-05543-x
Rodriguez, M. Y., & Storer, H. (2020). A computational social science perspective on qualitative data exploration: Using topic models for the descriptive analysis of social media data. Journal of Technology in Human Services, 38(1), 54–86. https://doi.org/10.1080/15228835.2019.1616350
Rothmüller, M., Rasmussen, P. H, & Vendelbo-larsen, S. A. (2018). Designing for interactions with automated vehicles: Ethnography at the boundary of quantitative-data-driven disciplines. EPIC – Ethnographic Praxis in Industry Conference, 2018(1), 482-517. https://doi.org/10.1111/1559-8918.2018.01219
Scheyvens, R., Banks, G., & Hughes, E. (2016). The private sector and the SDGs: The need to move beyond ‘Business as Usual.’ Sustainable Development, 24(6), 371–382. https://doi.org/10.1002/sd.1623
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