Are Whitepaper Claims Reflected in Market Structure? A Contamination-Aware Pipeline and a Power-Limited Null

Abstract

Do the functional narratives in cryptocurrency whitepapers correspond to how their tokens behave in markets? We develop a content-verified, contamination-aware pipeline for measuring structural correspondence between project narratives and market structure, and report two results. The first is a cautionary one. An apparent entity-level signal in an earlier version of our corpus -- specialised tokens appearing to align more strongly than broad infrastructure tokens -- was entirely an artifact of corpus contamination: roughly a quarter of the documents were failed-download stubs or wrong-document whitepapers (for example, a "Cosmos" entry that was in fact Binance Smart Chain text), and the apparent ordering does not survive content verification: on the clean corpus no token registers as helping alignment. We therefore report it as a contamination diagnosis, not a finding. The second is an honest null. Combining zero-shot NLP classification of 43 content-verified whitepapers across 10 semantic categories with seven cross-sectional market-structure statistics computed from hourly data (17,543 timestamps, 2023-2024), and aligning the two spaces with Procrustes rotation and Tucker's congruence coefficient ($\phi$), we do not detect a significant claims-market alignment in this $n = 43$ sample (dimension-matched $\phi = 0.303$, zero-padded $\phi = 0.223$; both non-significant). A positive-control and power analysis shows the binding constraint is the low reliability of the text instrument: the minimum detectable effect is $\phi \approx 0.66$, well above the observed $\approx 0.22$. This is absence of evidence for alignment, not evidence of its absence -- we can reject strong alignment ($\phi \geq 0.70$) but cannot distinguish weak alignment ($\phi \approx 0.3$) from none.