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The analysis of the social impact of research activity carried out in the area of selected social disciplines in the United Kingdom and in Poland was the main goal of the paper. The research process was conducted with the use of documents prepared for the evaluation procedures taking place in the United Kingdom in 2014 and in Poland in 2022 and presenting cases of the impact of research activity on society and the economy. This analysis, performed with the use of exploratory text analysis methods (particularly with the latent Dirichlet allocation model), allowed to identify topics and evaluate their significance in documents prepared by individual universities or groups of universities. A comparative analysis of the contents of descriptions was also performed. All the analyses presented in the paper were conducted by the authors with the use of computer programs written in the R language.(original abstract)
Twórcy
autor
- Kracow University of Economics, Poland
autor
- Krakow University of Economics, Poland
autor
- Krakow University of Economics, Poland
Bibliografia
- Blei, D., Ng, A., & Jordan, M. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
- Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1). https://doi.org/10.1214/aoms/1177729694
- Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic keyword extraction from individual documents. In Text Mining: Theory and Applications. John Wiley & Sons.
- Sievert, C., & Shirley, K. (2015). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63-70. Association for Computational Linguistics. https://doi.org/10.3115/v1/w14-3110
- Wróblewska, M. N. (2017). Ewaluacja "wpływu społecznego" nauki. Przykład REF 2014 a kontekst polski. Nauka i Szkolnictwo Wyższe, 1(49). https://doi.org/10.14746/nisw.2017.1.5
- Wróblewska, M. N. (2021). Research impact evaluation and academic discourse. Humanities and Social Sciences Communications, 8(1). https://doi.org/10.1057/s41599-021-00727-8
- Yao, L., Mimno, D., & McCallum, A. (2009). Efficient methods for topic model inference on streaming document collections. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/1557019.1557121
Typ dokumentu
Bibliografia
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bwmeta1.element.ekon-element-000171701360