Weighting in-text citation location and context meanings for proposing citation quality score metric

Purpose: The assessment of citation value necessitates considering the number of citations and the specific characteristics of in-text citations. This article establishes the significance of location and contextual meaning attributes in determining the citation value. It also aims to create a framework for assessing the quality score metric of citations. Design/methodology/approach: The ground truth weights are created using expert opinion through the best-worst method (BWM). Automatic weighting is required for future metric development. Therefore, ground truth weights are compared to five objective multi-criteria decision-making (MCDM) methods: (1) standard deviation, (2) Gini coefficient, (3) entropy, (4) criteria importance through inter-criteria correlation (CRITIC) and (5) method based on criteria removal effects (MEREC). The selected weights are then utilized to create the metric framework. Findings: The highest ground truth weight for the citation attributes is in the method section location (0.2176), positive sentiment (0.4455), role source from the method (0.3537) and function as a comparison (0.2554). The objective method closest to the ground truth is MEREC. Three types of citation quality score (CQS) metrics are developed to assess the significance of citation in a single appearance in a text, within a citing document and across all available data. Notably, a significant correlation exists between proposed metrics and expert judgment. Originality/value: This article presents a novel citation metric that prioritizes quality factors over traditional metrics, focusing solely on numbers. Comparing various MCDM approaches is a novel approach to scientific measurement research.