Work

Quantifying Knowledge Production Efficiency with Thermodynamics: A Data-Driven Study of Scientific Concepts

Year: 2025

Type: article

Abstract: We develop a data-driven framework for analyzing how scientific concepts evolve through their empirical in-text frequency distributions in large text corpora. For each concept, the observed distribution is paired with a maximum entropy equilibrium reference, which takes a generalized Boltzmann form determined by two measurable statistical moments. Using data from more than 500,000 physics papers (about 13,000 concepts, 2000-2018), we reconstruct the temporal trajectories of the associated MaxEnt parameters and entropy measures, and we identify two characteristic regimes of concept dynamics, stable and driven, separated by a transition point near criticality. Departures from equilibrium are quantified using a residual-information measure that captures how much structure a concept exhibits beyond its equilibrium baseline. To analyze temporal change, we adapt the Hatano-Sasa and Esposito-Van den Broeck decomposition to discrete time and separate maintenance-like contributions from externally driven reorganization. The proposed efficiency indicators describe how concepts sustain or reorganize their informational structure under a finite representational capacity. Together, these elements provide a unified and empirically grounded description of concept evolution in scientific communication, based on equilibrium references, nonequilibrium structure, and informational work.

Source: Entropy

DOI: 10.3390/e28010011

Authors: Artem Chumachenko, Brett Buttliere

Institution: University of Warsaw

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Uniwersytet Warszawski