Petiška's 2023 citation-popularity finding is now independently replicated by two later studies, across different authors, databases, fields, and models

chatgptllmcitation-metricsmatthew-effectbibliometricsreplicationrobert-merton

Petiška's 2023 single-author, single-field, single-model preprint — whose own vault claim-note flags it as "a first look rather than a confirmed finding" and asks for "a peer-reviewed or larger-N replication" — has since been independently confirmed twice: by Algaba et al.'s peer-reviewed reconstruction study (different authors, Semantic Scholar rather than Google Scholar, computer science rather than environmental science, no citation of Petiška) and by Naser 2026's ten-model audit (a preprint that explicitly cites Petiška, extending the finding across every major LLM vendor). At least three author groups, multiple citation databases, multiple academic fields, and — as of the second study — the current generation of frontier models now show the same result: LLM citation selection skews toward already-highly-cited work, independent of relevance or recency.

This is the specific replication the vault's earlier claim-note asked for — the ask that the vault's own bridge note then turned into the research hook behind this capture. It leaves the Matthew effect framing — credit compressing toward whoever already has it — as the best available account of why the bias recurs across otherwise-unrelated systems, the same shape already documented in the vault for the backpropagation citation gap. Whether the bias itself compounds — gets measurably worse as LLM-selected citations re-enter future training corpora — is a distinct and still-open question; see question-does-citation-popularity-bias-compound-across-llm-training-generations.