Work

Layer 4: Editorial Synthesis as a Governance Function in Scholarly Publishing

Year: 2026
Type: article
Abstract: Background: Peer review remains central to scholarly publishing, yet increasing reviewer scarcity, delayed reviews, and fragmented evaluative input challenge traditional editorial workflows. Recent discussions of artificial intelligence (AI) have focused largely on its potential role in evaluation, while comparatively little attention has been paid to its use in supporting editorial synthesis and decision-making. Purpose: To describe and examine Layer 4, a framework for AI-assisted editorial synthesis that supports governance and decision-making under conditions of reviewer scarcity and informational fragmentation. Methods: A qualitative editorial case series examined 5 consecutive manuscripts processed through a structured Layer 4 workflow. AI-assisted tools were used to organize reviewer comments, identify areas of convergence and divergence, and support development of synthesis notes and decision language. Cases were analyzed to identify recurring functional patterns. An additional illustrative example demonstrated editorial synthesis using multiple human and AI-assisted reviews. Results: Four recurring Layer 4 functions were identified: routine synthesis, capacity compensation, arbitration between divergent inputs, and diagnosis of non-convergence. Across cases, Layer 4 supported editorial reasoning when reviewer input was sparse, delayed, or conflicting. The illustrative example showed that Layer 4 functioned not as a voting mechanism but as a process of editorial identification that transformed evaluative evidence into coherent judgment. Conclusions: Layer 4 is proposed as a governance mechanism that supports editorial synthesis while preserving human responsibility for decisions. Reviewer reports provide evidence; editorial decisions require judgment. As reviewer scarcity and manuscript numbers increase, structured AI-assisted synthesis may help sustain accountable editorial decision-making without replacing peer review or editorial authority.
Language: English
Open Access status: green
OpenAlex ID: W7225333811
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