Page 210 - Journal of Library Science in China, Vol.47, 2021

Ronda J. ZHANG & Fred Y. YE / Measuring knowledge hardness for quantifying backbone knowledge 209

solidifies the bond between the measurement of knowledge and Library and Information Science.
Consequently, discussing the measurement of knowledge hardness in the Journal of Library
Science in China possesses logical coherence and relevance.
Given the inherent characteristic of academic subjects to undergo dynamic knowledge updates,
the entities assessed by knowledge hardness are inevitably academic objects. As these academic
objects coalesce into objectively recognized disciplines, the standard textbooks of each discipline
become paramount in representing the hardness of the knowledge within that field.
Along with knowledge metrics and scientific metrics [18, 12-13, 19] , for defining knowledge hardness,
certain foundational principles are indispensable. When considering knowledge hardness and its
metrics based on academic objects, the proposed guidelines are as follows:
Rule 1: The primary criterion for measuring knowledge hardness is the standard textbook of the
discipline.
Mature disciplines typically have universally recognized standard textbooks. For instance,
physics is represented by the Feynman Lectures on Physics and Landau’s Course of Theoretical
Physics, while economics boasts foundational texts like Marshall’s Principles of Economics,
Samuelson’s Economics, and Mankiw’s Principles of Economics. This establishes both the
potential and feasibility for assessing the knowledge hardness within a discipline. For fields that
lack a definitive standard textbook, general textbooks or those with widespread adoption can serve
as reference standards.
Rule 2: The fundamental method for measuring the knowledge hardness of a discipline is by
quantifying its phenotypic characteristics.
Based on the understanding that knowledge primarily manifests in three forms—text, formulas,
and diagrams [17] —a straightforward equation can be proposed for the assessment of knowledge
hardness, thus providing a systematic approach for gauging the rigidity of disciplinary knowledge.
Rule 3: Under consistent measurement conditions, the knowledge hardness across different
disciplines can be compared.
This rule offers a foundation for contrasting the knowledge hardness across diverse fields,
thereby allowing for the delineation of core knowledge disparities among them and facilitating a
quantitative comparison of their knowledge hardness.
Following the aforementioned logic and principles, one can measure knowledge hardness across
disciplines and analyze the core knowledge within each subject.
Many classical disciplines often present their core knowledge using formulas and diagrams. For
instance:
● In mathematics, areas like number theory, geometry, and analysis involve formulas such as
Euler’s formula and the Newton-Leibniz formula.
● In physics, classical mechanics, quantum theory, and relativity employ equations like the
Lagrangian equation, Hamilton’s equation, and Schrödinger’s equation.
● Essential diagrams in chemistry and biochemistry include the periodic law of elements and the