Metrics and Mechanisms: Measuring the Unmeasurable in the Science of Science

TL;DR

This paper explores how emerging metrics can reveal the hidden mechanisms of science, linking quantifiable data to fundamental scientific processes to improve understanding and development of scientific metrics.

Contribution

It introduces a classification of scientific mechanisms and emphasizes the importance of understanding these mechanisms to develop effective metrics.

Findings

Emerging metrics complement existing ones by revealing hidden structures.

Classification of science into hot/cold, fast/slow, soft/hard reveals underlying mechanisms.

Understanding mechanisms guides better development of scientific metrics.

Abstract

What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics.