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This article introduces the concept of "citation pathways" to enhance the analysis of scholarly impact by focusing on the intermediate nodes of knowledge transfer, specifically through Interpretive Knowledge Nodes (IKN) and Citation Compression Layers (CCL). By analyzing how AI has altered the cost structure of producing citable knowledge intermediaries, the authors argue that citation pathways could significantly influence the validity of impact measurement in academia. The findings suggest that traditional citation metrics may overlook critical dynamics in knowledge dissemination, raising concerns about incentive misalignment in scholarly publishing.
Citation pathways reveal that the true impact of academic work may be obscured by traditional metrics, highlighting the importance of knowledge intermediaries in shaping scholarly influence.
This article identifies"citation pathway"as a long-neglected analytical dimension in scientometrics. Traditional evaluation metrics focus on measuring citation counts while paying insufficient attention to the intermediate nodes through which knowledge flows from its original source to the citing author. Building on an analysis of the normative structure of current reference systems, this article introduces two new concepts: Interpretive Knowledge Nodes (IKN) - academic papers that provide structured reorganizations of classic works - and Citation Compression Layers (CCL) - the intermediate layers that emerge when such knowledge products acquire stable publication identities and enter formal citation networks at scale. The central proposition is that AI has not changed citation rules themselves but has transformed the cost structure of producing citable knowledge intermediaries. Under conditions of full compliance, the network position effects of citation pathways may become a salient variable affecting the validity of impact measurement. Through a thought experiment involving a hypothetical journal R and a parsimonious"Citation Gravity conceptual model,"this article substantiates this proposition and discusses the measurement boundaries of scholarly impact indicators, as well as the institutional risk of incentive misalignment under extreme scenarios.
