The Matthew Defect: How Credit Economies Bury Ideas, Distort Careers, and Mis-train Our Machines

The Matthew Defect (MD) is the systematic burying of ideas and their originators through standard academic practices that reward prestige over provenance. Unlike Merton's Matthew Effect, which demonstrates how advantage accumulates for those already recognized, the Matthew Defect reveals how evaluation systems actively disadvantage specific work through routine procedures rather than misconduct. This dynamic emerges from everyday practices including editorial gatekeeping, strategic citation, and metrics that substitute institutional prestige for substantive judgment. These patterns, already intensified by digital platforms and hypercompetition, now risk being permanently embedded in Large Language Models that inherit and amplify skewed citation patterns from their training data. I introduce KNOBE (Knowledge-Native Objects for Bots and Engines) as a proposed infrastructure for preserving provenance in human-AI knowledge systems.