Judging AI-Native Work: Gates, Evidence, and the Limits of the Machine

Abstract

A new category of creative and technical work has arrived faster than the means to judge it. This working paper argues that credible recognition of AI-native work rests on three commitments that must hold together: admissibility before merit, evidence before claim, and permanently human recognition authority. It explains the five admissibility gates, the role of versioned workflow evidence, the Film-domain measurement architecture, and the institutional firewall that permits computational assistance to organize information while prohibiting machine systems from exercising recognition authority. Institutional disclosure: The Orchestrator Institute and Orchestrator Awards are sibling institutions under Celestial Technologies LLC. The author holds an institutional interest, stated so readers can weigh it. The paper was drafted with AI assistance under the author's direction and final authority. All claims, positions, and errors remain the author's. Non-activation statement: Publication does not activate scoring, evaluation, judging, submissions, payments, nominations, juries, voting, or recognition. Doctrine: Publish first. Be cited second. Recognize third. Series: Orchestrator Awards Working Papers · Issue 003. Review status: Working paper — not peer-reviewed.