Landscape of Science. Part 1. Methodology

Abstract

The concept presented in this article proposes a multidimensional approach to analyzing the scientific space, viewing it not as a static set of disciplines, but as a set of actually occurring knowledge production processes determined by various factors. These processes are analyzed through the filters of various ‘modes of research observation’; various layers of mental (if possible, theoretical) understanding of this reality are isolated and projected from empirical reality. This methodological approach avoids reductionism and reveals the hidden causes of processes indiscernible in traditional analysis. Modes of scientific analysis are formed historically, as qualitatively different types of determination are discovered. They shape the course of processes in the scientific space. We employ four modes: epistemic – identifying the generative determination that explains how knowledge is produced; axiological – identifying the normative determination that defines what is considered valuable and for what purposes knowledge is produced; institutional – identifying the regulatory determination which establishes the rules, norms and control mechanisms that organize the scientific process; geopolitical – demonstrating the configuring determination that shows how the interaction of countries and their blocs influences the global scientific landscape, shaping the channel/space of possibilities. Next, we identify the current vectors of change in these layers, defining dynamic coordinate axes. With their help, changes in the scientific landscape can be characterized through quantitative data such as rankings and indicators. Overall, our framework allows us to move from describing individual trends to analyzing the holistic configuration of the scientific landscape, bridging the gap between a fragmented and a systemic perspective. The model also takes into account the cascade structure, where changes in one layer shift the configuration of others, and the factor of temporality (compression of the “hypothesis – implementation” cycle), which creates systemic stress for institutions. This opens the way to solving a practical problem: developing an operational model of the scientific space suitable for AI analysis of big data and forecasting structural shifts in the modern scientific landscape. The methodological novelty of this approach lies in identifying points of greatest sensitivity and potential entry points for deliberate institutional changes. Thus, the model not only describes but also offers tools for managing development in the scientific space.