Typology of professional trajectories of gifted individuals using neural network analysis

Abstract

Based on a data set (100 biographies) created by the authors through content analysis of biographical material about outstanding scientists of the 19th and 20th centuries in the humanities and natural sciences, the clustering of professional trajectories of gifted individuals was carried out. Neural network analysis based on self-organizing Kohonen maps was used as a clustering method. The professional trajectories were formed within the framework of the behavioral model of the linear-stage approach to studying life cycles. Within this approach, career and professional self-realization are understood as a sequence of evolutionary stages fixed in their order of occurrence. Each stage was encoded, and the biographies were transformed into a vector system. In turn, the task of clustering consisted in dividing a hundred vectors into typical groups with several real-valued coordinates. The criteria for the quality of clustering were the minimum sum of quantization errors and the silhouette coefficient. As a result of the study, seven professional trajectories of gifted individuals were identified and interpreted. The analysis of trajectories was carried out from the point of view of the speed of success (average age of success) and those factors and conditions of the life path that could affect either rapid or slow achievement of professional goals and self-realization. This example demonstrates the possibilities and limitations of using neural network analysis for solving similar research tasks, especially when working with complex cluster forms and finding their optimal number.