Calculating a source’s value: how to score credibility and contribution

Calculating a source’s value involves assessing both its credibility and its unique contribution to a conclusion, rather than relying solely on how often it appears in search or AI results. Traditional evaluation frameworks such as CRAAP and SIFT help users examine authority, currency, accuracy, purpose, and corroboration, while the proposed scoring approach adds relevance, objectivity, and uniqueness, assigns task-specific weights, and recommends excluding sources with severely deficient accuracy regardless of their overall score. The discussion distinguishes scholarly, popular, and trade publications as initial indicators rather than guarantees of quality, and emphasizes lateral reading, tracing claims to original records, and checking whether information applies to the relevant population and time period. For AI agents, contribution can be estimated through methods such as Shapley value, which measures how an answer changes when a source is removed, although contribution and credibility must remain separate because influential sources can still be wrong. Research cited in the text suggests that source visibility often differs from irreplaceable value, with original reporting, official records, proprietary data, and primary sources frequently offering greater uniqueness than widely repeated material. The text ultimately advocates incorporating retrieval controls, freshness checks, deduplication, citations, confidence ratings, and human review into agent research pipelines so that sources are evaluated systematically and transparently.

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