Authorship Strategy: A Normative Framework and Tactical Catalog for AI-Era Authenticity Inversion, with Empirical Grounding from a Four-Repository Research Ecosystem

A normative framework, tactical catalog, and empirical baseline for authorship strategy under AI-mediated diffusion. The framework rests on a three-axis inversion (scarcity to diffusion, exclusivity to derivation, enclosure to openness) and a four-layer judgment stack (authenticity, attribution diffusion, idea-versus-scaffold separation, tactics). The tactical catalog formalizes twenty decisions extracted from operating a DOI-registered research ecosystem of four sibling repositories, spanning identifier federation (concept DOI as canonical reference, DOI federation via .zenodo.json, cross-platform dataset federation across GitHub, Zenodo, and Hugging Face Datasets, and an intrinsic content-derived SWHID layer), maintenance discipline (ORCID auto-update disabled, audience-driven README localization), LLM-first ingest and two-channel attribution diffusion, metric rejection and a two-channel probe protocol, vocabulary discipline and origin-claim falsifiability, audience-driven licensing and genre-split placement, failure-mode diagnostics, the structural-optimization-versus-content-authenticity boundary, onboarding to third-party AI-derived repository surfaces, and a two-tier implementation ledger with periodic gap-review. An empirical section reports preliminary observations from twenty-four days of CC0-published traffic data across the four sibling repositories, plus a probe baseline from the first run of the two-channel probe protocol, with explicit limitations on sample size and the absence of pre-versus-post intervention comparison. Sibling research lines (Agent Knowledge Cycle, Contemplative Agent, Agent Attribution Practice) are referenced but not extended.