The Limits of Rules
Rules fail when context changes, complexity increases beyond anticipation, or people game them by optimizing the rule instead of the intended goal.
What was corrected
The article cited a 2012 National Audit Office report claiming 50 percent of NHS trusts had manipulated waiting-time data, when the real, verifiable NAO figure is far smaller; described a Mother Teresa hospice project for dying AIDS patients that spent eight years and 284 permit applications across 18 agencies seeking approval, when the real, well-documented story is about a homeless shelter that took roughly two years and stalled over a single building-code elevator requirement; cited an unverifiable 2015 Common Good survey statistic; reversed the author order on a real Swiss tax-compliance study (citing 'Frey and Feld' rather than the correct Feld and Frey); cited a 2009 Leuz, Triantis, and Wang paper with a figure and a claim about companies 'going private' that actually contradicts the real 2008 paper's finding, which was about companies deregistering rather than going fully private; cited the Dyck, Morse, and Zingales fraud-detection study with the wrong publication year; and added a claim that a real 2016 BMJ paper on medical error connected its findings to regulatory burden, when the actual paper is about death-certificate coding and does not address regulation at all.
The section now describes the NHS waiting-time gaming pattern without the incorrect NAO percentage, describes the real Mother Teresa homeless-shelter story with its actual timeline and the real building-code dispute, removes the unverifiable Common Good survey figure, corrects the Swiss tax-compliance citation to Feld and Frey, describes the Leuz, Triantis, and Wang findings accurately as being about firms deregistering rather than going private, corrects the Dyck, Morse, and Zingales paper's publication year, and describes the Makary and Daniel BMJ paper accurately as a paper about death-certificate coding of medical error, without an added regulatory-burden claim the paper does not make.
Why this is better
Web searches for each citation and case-study detail found a consistent pattern: real researchers, books, and organizations with specific figures, dates, or story details that did not match the documented record, including one instance where a cited paper's real finding was the opposite of how it was described. Since the underlying research and general arguments are well established, the sections were rewritten to correct or remove the specific inaccuracies rather than deleting the material outright.
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