A Machine Learning Model Optimising Publication Strategies within the Evaluation Framework of Polish Research Institutions – A Positional Goods Approach

Abstract

This paper proposes a machine learning model applied in order to optimise publication strategies of Polish research institutions evaluated under the Polish national science assessment system. Drawing on the theory of positional goods, the proposed model reflects the competitive and relative nature of scientific value, where success is defined not absolutely but in relation to others. By aligning artificial intelligence-based optimisation with institutional predefined goals, the model supports better allocation of research resources and adapts dynamically to changes in evaluation criteria and competitor behaviour. The study presented in this work also addresses key legal concerns, including the transparency and legitimacy of using algorithmic tools in public sector decision making. It contributes to legal informatics by exploring the potential of artificial intelligence (AI) to support advisory processes without violating constitutional principles such as legality or institutional autonomy. The integration of AI with positional goods theory offers new insights into managing scientific competition and opens avenues for regulatory simulations and improved strategic planning in higher education.

Ähnliche Arbeiten