Paradigm shifts in science are visible in what researchers assert and contest before they are visible in the citation graph. We ask whether a cheap, content-level signal of epistemic instability, derived from the changing distribution of stated modelling beliefs in paper abstracts, can anticipate a paradigm shift earlier than the dominant citation-based disruption index (CD5). On the displacement of recurrent networks by Transformers in NLP, a pre-registered content signal crosses its detection threshold in 2016-Q1, whereas a real-time CD5 monitor cannot even observe the 2017 breakthrough until 2022-Q2, since CD5 needs a five-year forward-citation window: a lead of about 25 quarters. The flat citation baseline is not an artifact of one index, as our CD5, a reference-normalised variant, and an authoritative precomputed index all sit near zero across the shift. The lead is also not a faster proxy: at equal latency the signal beats the content competitors tested, including a learned CD-from-text model and an embedding disruption measure, and the decomposition sees what a keyword cannot (keyword-blind AUC of about 0.9, reproduced on human labels). The lead over CD5 is an observability lead. Which signal carries it depends on the shift: belief adoption in NLP, contestation and applicability stress in vision. Measured against the breakthroughs themselves, the signal leads by five quarters in NLP and is contemporaneous in computer vision.

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