Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI

• URL: http://arxiv.org/abs/2606.12713v1
• Date: Wed, 10 Jun 2026 22:04:56 GMT
• Title: Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI
• Abstract summary: "AGI" lacks a single shared and stable referent and competing operationalizations can return different verdicts on the same system.<n>This article treats that under-specification as a design and governance problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Claims that artificial general intelligence has already arrived and claims that it remains decades away are often defended from overlapping evidence. "AGI" lacks a single shared and stable referent and competing operationalizations can return different verdicts on the same system. This article treats that under-specification as a design and governance problem. Following Design Science Research Methodology, it develops DAF-AGI, a second-order conceptual artifact with two coupled components: five ordinal criteria for assessing the adjudicative fitness of candidate definitions and a structured governance audit of authorship, interest, certification, external verification and revision authority. The artifact is demonstrated on five prominent measurement families and one deflationary boundary position in a documented corpus and then stress-tested against a stylized strong arrival claim: that current generative systems constitute AGI because they outperform a well-educated adult on many cognitive tasks. On evidence from the cited 2024-2025 sources, the claim was certifiable only under a performance-based operationalization; capability-ontology, psychometric and skill-acquisition approaches did not certify it, the economic family remains indeterminate and the deflationary position refuses binary adjudication. The contribution is a novel integration and operationalization, not an empirical validation: independent application, inter-rater testing and author-external cases remain necessary. The paper further proposes definitional sovereignty as an enabling component of algorithmic sovereignty: the institutional capacity to contest, certify and revise imported technological categories under public accountability.

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