Library Coverage
RAG grounding & fintech adoption — Can retrieval-grounded drafting reduce unsupported claims in empirical manuscripts?
Warm Library Coverage synthesis (scaffolded). Assess composition and continuity — not live LLM quality.
## Field synthesis — submission draft ### Retrieval & accountability RAG improves what the model can *see* but not what the author can *defend*. Faithfulness metrics and atomic fact scores (FActScore) justify verify-first UI: show passage linkage before export, not after desk reject. ### Adoption mechanisms (illustration only) Trust and perceived usefulness consistently dominate behavioral intention in adoption models. Ease of use is real but often marginal when trust is in the model—matches our β ordering (Trust > PU > PEOU). ### Cross-base risk Mixing RAG surveys with fintech adoption papers is fine for a **methods** paper with empirical illustration, but avoid claiming retrieval benchmarks validate substantive fintech effects. ### Next moves before submit - Resolve open passage comments (4 open). - Accept adjusted R² suggestion in Results. - Run citation trace on empirical paragraphs. - Soften any remaining “elimination” language in Discussion.
Active research lane
Grounded drafting & fintech adoption
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