Does Simkin & Roychowdhury's copying-driven citation skew — rather than citation-count inflation generally — explain the training-corpus distribution behind GPT-4's citation-popularity bias?
Raised while promoting a 2026-10-15 capture, itself the forager's second run at the same cosine-0.883 seed pair that the 2026-10-14 hop already adjudicated as an embedding false friend at the mechanism level. That note names one live thread it left untested: "whether Simkin's copying model explains the training-corpus skew Algaba's first hypothesis takes as given — upstream of the finding, not a competitor to it." This question operationalizes that thread.
Algaba et al. propose, as "one plausible hypothesis," that GPT-4's citation bias comes from "the increased frequency of citations to heavily cited papers within the model's training data" — a claim about the training corpus, not about why the corpus looks that way. Simkin & Roychowdhury's copying model explains one route to a citation ecosystem's skew toward already-famous papers: reference-list copying, where "a majority of scientific citations are copied from the lists of references used in other papers," mechanically feeding a paper's count forward into its own future odds. But citation-count inflation has other routes too — independent merit recognition, media attention, open-access availability, institutional prestige — and no located source tests whether the training-corpus skew Algaba's hypothesis takes as a bare fact is itself attributable to Simkin-style copying specifically, as opposed to one of these other routes. Mozafari et al. 2026 directly confirms the general "LLM preference is inherited from training-corpus exposure" mechanism-class — for entity popularity broadly, using OLMo/Dolma's open training data — but for entities, not papers or citation counts, and without engaging copying as a candidate cause of the exposure distribution itself.
What would resolve this: a study that (a) takes a citation ecosystem whose skew is independently shown — e.g. via misprint-fingerprinting, as in Simkin & Roychowdhury's own 2002 method — to be copying-driven rather than merit-driven; (b) traces that same ecosystem's text into an LLM's training corpus (an open-training-data model, per the Mozafari precedent, since a closed model like GPT-4 cannot be audited this way); and (c) shows the LLM's output bias tracks the copying-attributable portion of the skew specifically, not citation count in general. No such study was found across a targeted search (multiple query formulations, direct inspection of the Algaba and Mozafari primaries).
Not the same question as question-does-citation-popularity-bias-compound-across-llm-training-generations, which asks whether the bias strengthens across successive model generations as LLM-selected citations re-enter training data. This question is about the human ecosystem's citation skew — whether it is copying-produced in the first place — not about whether LLM output feeds back into it.
Progress log
- 2026-10-16: Opened at promotion. No source splices the two mechanisms; the gap is real and specific, not a search failure — Simkin & Roychowdhury's paper predates LLM citation behavior by two decades and makes no claim about machine training, and Algaba et al.'s Discussion proposes training-data frequency without citing Simkin, Roychowdhury, or any copying-based account of why that frequency distribution looks the way it does. GhostCite's AugMix finding — propagation traced to a shared tool rather than reader copying — is a reminder that "looks like inherited copying" and "is inherited copying" have come apart before in this vault's own material, which is one more reason this question wants a direct test rather than a plausibility argument.
- 2026-10-18: A second, independent search session (one day later, fresh query formulations) reconfirms the gap rather than closing it: no source located splices the copying mechanism to the training-corpus distribution. Two papers new to the vault were surveyed directly — Niimi 2025 (citation count as training-data-redundancy proxy; near-verbatim memorization above ~1,000 citations) and Liu et al. 2026 (six-LLM masked-citation audit affirming Algaba's popularity-bias finding) — and neither tests, cites, or engages a copying-specific account; Liu et al.'s full text does not use the word "copying" anywhere. Still open.