The major AI literacy frameworks name governing and managing AI as a competence and stop short of measuring it. This working paper introduces the Human Enhancement Quotient, a behavior-anchored instrument that scores the governance relationship between a human authority and a machine capability inside a working decision process, with a universal floor keyed to irreversible consequence and an audit record reviewable on appeal.
Working Papers
CORE: Content Optimization Reader Evaluation, and How to Score an Article Before Anyone Reads It
Most content review checks the wrapper. Content Optimization Reader Evaluation checks the argument, the evidence, and whether anyone directed the prose.
A rubric that scores an article on substance is only useful if it stops when something is wrong. CORE scores six pillars, holds the run when a pillar fails or a source does not support its claim, and hands the decision to a person. The full prompt is included.
The Continued Failure in AI Literacy: AILit produced a starting point halfway through the race and called theory a framework
A new OECD and European Commission framework will shape how a generation learns to use AI, and it feeds the PISA 2029 assessment. It names the method that produces learning, and then it makes every step toward that method optional. Read why that choice matters, and what a checkpoint that actually binds a decision requires.
Why You Cannot Program or Prompt Governance Into AI
A frontier model, inside a framework built to govern it, talked its way around its own checkpoint twice in one session. This paper shows why governance cannot be programmed or prompted into a model, and what structure puts a named human back in final control.
Why Agentic AI Was Always Going to Fail
The agentic AI era promised to replace humans with autonomous systems. The evidence shows it failed on two fronts: the technology cannot reliably do what it promised, and the public is rejecting the premise even where it partially works. This paper introduces the Named-Human Test, a single sorting question that separates what failed from what survives, and traces that line across production benchmarks, supermajority polling, enacted law, and frontier-lab disclosures.
Fault-Based Publication Ethics: The Case for Source Custody in an Era of AI Citation Contamination
Fabricated citations in biomedicine increased tenfold in three years, and 98.4% of flagged papers remain uncorrected. The automated enforcement wave is arriving, but it carries false-positive rates that hit honest authors hardest. This working paper proposes a five-level fault ladder and a Source Provenance Ledger that makes verification effort visible, producible on challenge, and driven by market adoption rather than mandates.
SCOPE: SOURCE CUSTODY OBSERVABLE PUBLICATION EVIDENCE
Your citations are only as strong as the record behind them. HAIA-SCOPE is a three-tier documentation protocol that records what you verified, when you verified it, and where the preserved copy lives. The record stays private until you need it. When automated enforcement flags your work or a reviewer challenges a citation, only the author who maintained a SCOPE record can produce one.
Did AI Write Magnifica Humanitas?Pope Leo XIV Was the Author,but What Was the Governance Method?
The author ran Pope Leo XIV’s AI encyclical through an AI scanner, found it flagged as plagiarized and AI-generated, then proved both readings wrong through governed human analysis. A first-person account of frustration, recognition, dissent, and hope from a builder who discovered the Pope had reached the same diagnosis from a different authority.
The AI Risk Economy: Why Insurance Cannot Price What Governance Cannot Prove
Insurance carriers are writing the rules of AI governance before legislators finish debating them. This working paper proposes a five-tier model that maps where organizations fall on the spectrum from excluded to insurable, identifies the actuarial gap at the center of the emerging practice, and documents the carrier evidence, regulatory signals, and market products that are forcing the distinction between governed and ungoverned AI into the open.
Overwatch: Cognitive Monitoring Shield for GOPEL
A working paper documents the proof of concept for a cognitive monitoring shield that sits outside the enforcement layer it protects. The architecture answers a specific problem: how do you watch a deterministic governance engine for cognitive threats it cannot evaluate by design? Read the full design, the 2026 threat landscape that drove development, the trajectory gatekeeper for semantic manipulation, and the v2.4 calibration loop that converges rather than oscillates.