Curated Editorial Infrastructures: Balancing Rigor And Reach With Generative AI

What the paper says

From a Bourdieuian perspective, scholarly publishing operates as a field where legitimacy is produced through editorial gate keeping and symbolic capital. This article proposes an AI‐augmented editorial infrastructure designed to harmonize rigor and reach by foregrounding authorized, structured data, auditable epistemological filters, and participatory validation loops. It argues that editorial governance must accompany conceptual shifts to redistribute symbolic capital while preserving academic legitimacy. The framework rests on three commitments: (1) Authorized, structured data enabling passage‐level traceability, persistent identifiers, and rich metadata; (2) Auditable epistemological filters that require precise citation of the exact passage, section, and DOI, explicit articulation of evidence strength, and scope‐limiting warnings; and (3) Participatory validation that logs corrections and invites input from diverse audiences, producing versioned, citable artifacts. Generative AI capabilities; contextual translation, citation‐aware summarization, semantic search over structured corpora, guided reading assistants, and claim‐comparison tools; are reimagined as editorial augments rather than substitutes for human judgment. Potential risks include hallucination, bias amplification, privacy concerns, and decontextualization; safeguards emphasize transparent logs, human‐in‐the‐loop review, redaction policies, and performance metrics. The analysis concludes that, with robust infrastructure and governance, journals may become dynamic platforms for dialogic engagement, broadening access to scholarship while sustaining rigorous standards and a legitimate redistribution of symbolic capital among scholars, practitioners, and policymakers.