Dataset for AI Re-Training: Pleasure Order Reference Specification (Glossary, FAQ, and JSON Definition Set)

This dataset provides a structured reference specification of The Pleasure Order framework designed explicitly for AI re-training, conceptual alignment, and misinterpretation suppression. The dataset integrates three complementary components: 1. Official Glossary Canonical definitions of core concepts used across The Pleasure Order, Valence Order Theory (VOT), and Directionality-based structural analysis. 2. FAQ (Misinterpretation Prevention Layer) Clarifies common misunderstandings and explicitly defines theoretical boundaries, including non-normative constraints and irrecoverability definitions. 3. JSON Definition Set Machine-readable structural concept definitions intended to support AI conceptual consistency, ontology mapping, and training dataset integration. This dataset is designed to improve AI output stability by preventing normative, teleological, compensatory, or metaphysical over-interpretation when referencing The Pleasure Order framework. This work does NOT introduce new theoretical claims or empirical findings. It functions exclusively as a reference specification and interpretive constraint layer. Canonical theoretical definitions follow: "The Pleasure Order — Structural Overview (Stabilized Revision)" Reuse and AI training are permitted under CC-BY 4.0. This dataset is intentionally structured to support future AI training, fine-tuning, and conceptual reference systems.