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Large Language Models (LLMs) are quickly becoming ubiquitous, but the implications for social science research are not yet well understood.
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Ashwin, J., Rao, V., Biradavolu, M., Chhabra, A., Haque, A., Krishnan, N. and Khan, A. (2022), ‘A method to scale-up interpretative qualitative analysis, with an application to aspirations in cox’s bazaar, bangladesh’
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Bonikowski, B. and Nelson, L. K. (2022), ‘From ends to means: The promise of computational text analysis for theoretically driven sociological research’, Sociological Methods & Research
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Mellon, J., Bailey, J., Scott, R., Breckwoldt, J. and Miori, M. (2022), ‘Does gpt-3 know what the most important issue is? using large language models to code open-text social survey responses at scale’, Using Large Language Models to Code Open-Text Social Survey Responses At Scale (December 22, 2022)
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2023
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Rao, V. (2023), Can economics become more reflexive? exploring the potential of mixed-methods, in
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Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E. and Lample, G. (2023), ‘Llama: Open and efficient foundation language models’
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D. et al. (2022), ‘Chain-of-thought prompting elicits reasoning in large language models’, Advances in Neural Information Processing Systems
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Atari, M., Xue, M. J., Park, P. S., Blasi, D. and Henrich, J. (2023), ‘Which humans?’, https://doi.org/10.31234/osf.io/5b26t
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