klotz: source evaluation*

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  1. Dan Russell writes about distinguishing between true, correct, and valid claims and offers a six-step verification routine for navigating misinformation. He notes that people often confuse fluency with factuality and that each of the three questions—truth, correctness, and validity—fails in distinct ways requiring different checks. His practical steps include pinning down the claim, noticing emotional reactions, tracing evidence upstream, reading laterally, triangulating with independent sources, and calibrating confidence. He also addresses the specific challenge of verifying what AI tells you, noting that fluent, confident outputs break usual credibility cues, and recommends treating citations as claims to be checked rather than evidence to be trusted.
    - The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
    - Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
    - AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
    - The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.

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