Algorithmic mediation in open access journals: platforms, visibility and epistemic challenges

Abstract

This study examines how algorithmic mediation influences the visibility of Open Access journals, linking this phenomenon to broader discussions on data capitalism, techno-colonialism, and platformization. We argue that academic knowledge circulation is shaped by a paradox of mediation: the illusion of direct access to research objects, alongside increasing dependence on Big Tech infrastructures. Despite their centrality, search engines like Google remain largely invisible as mediators of scientific visibility. By analyzing traffic metrics from an Open Access journal in Communication and Information, we explore the role of algorithmic logics in shaping discoverability. Using data from Google Analytics and the journal’s CMS, we investigate six key indicators, including traffic sources, organic search patterns, and PDF downloads. Our findings reveal how search algorithms privilege certain knowledge formations, reinforcing epistemic dependencies that challenge digital sovereignty. We discuss how algorithmic sorting does not directly promote or suppress academic work, but rather governs its accessibility through logics of popularity, similarity, and profiling. This dynamic raises ethical concerns, particularly as the metrification of science increasingly aligns with platform-driven visibility metrics. While alternative circulation strategies—such as direct traffic and academic networking platforms—mitigate some challenges, they do not offset the structural dominance of Google/Alphabet in Open Access dissemination. Our research underscores the need for critical engagement with algorithmic infrastructures, advocating for regulatory frameworks and tactical interventions to promote epistemic autonomy in digital knowledge production. These findings contribute to broader debates on platform governance, knowledge equity, and the future of scholarly communication.