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- W3183453973 abstract "The adoption of machine-learning is apparent throughout science, technology and business, as we learn how to maximise its usefulness. There has been a similarly broad range of effects across empirical sciences, from biology to cosmology to social science, as machine-learning methods have been developed to analyse high throughput experimental data in novel ways. For example, in learning to detect articles related to the methodology of management sciences (MMS), the task is to assign a label of “MMS” or “not MMS” to any given publications identified in relevant bibliographical repositories (e.g., Scopus or Web of Sciences databases). This task seems to be even more complicated when one is searching for MMS publications written in non-English language. Our paper deals with methodological issues related to this undiscovered to date problem on the example of polish methodology of management sciences. To even approach the issue, first, the custom database needs to be created, as the coverage of the existing database is selective, and inherits characteristics of the core-periphery model of scientific production. We propose a procedure to do so, as well as employ machine learning, and explore its accuracy in analogue documents inclusion into custom bibliographic databases for the case of polish management methodology. Our results are highly suggestive and reveal the applicability of the proposed procedure, and consequently, allow for more context-aware literature reviews and bibliometrics. This may lead to bringing back the manager to the management." @default.
- W3183453973 created "2021-08-02" @default.
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- W3183453973 date "2021-08-01" @default.
- W3183453973 modified "2023-10-16" @default.
- W3183453973 title "In Search for the Context: Using Machine Learning for Creating Custom Bibliographic Databases" @default.
- W3183453973 doi "https://doi.org/10.5465/ambpp.2021.15480abstract" @default.
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