From Quality to Visibility: Cumulative Advantage, Networked Attention, and Generative Search in Scientific Recognition

Scientific recognition is only weakly determined by the intrinsic quality of research. A large body of work in the sociology of science, bibliometrics, and the emerging science of science instead describes recognition as a networked, cumulative-advantage process: attention concentrates on work that is already visible, early advantages compound, and most papers attract little notice regardless of merit. This review synthesizes that literature across three layers. First, it surveys the structural mechanisms — the social construction of recognition, heavy-tailed citation distributions and preferential attachment, the Matthew effect and reputation thresholds, the asymmetry of credit in team science, and the timing of individual impact. Second, it reviews the evidence on deliberate dissemination interventions — open access, preprints, plain-language summaries, targeted outreach, social-media presence, and the activation of weak ties — distinguishing well-supported effects from contested ones. Third, it examines how large language models and generative search are becoming a new amplifier of cumulative advantage, with measured citation biases toward already-prominent work and a growing share of science-related information seeking mediated by generative engines. Throughout, the central implication is that visibility is an actionable, channel-dependent outcome rather than an automatic byproduct of quality. We close by considering where automated scholarly-visibility services fit within this evidence base, and we identify open questions for research on visibility in the generative-search era.This review was written by Boris Gorelik of Loud Camel — Academic Career Promotion, a service that operationalizes several of the dissemination practices reviewed here as a recurring workflow; its conclusions rest on the cited literature, not on the service.