Specifics and particularities of machine learning in philological R&D management amid turbulent and high-risk environments: A Shiva and Warmi perspective

Abstract

The article explores the features and specifics of the application of machine learning in the management of philological R&D in conditions of dynamic uncertainty, turbulence and systemic risks. The capabilities of ML for intelligent data analysis, forecasting, management of research resources and risks are determined. The need for an adaptive hybrid model "machine learning - expert - feedback" is substantiated, which meets the conditions of the SHIVA and WARMI environments.