This paper introduces autological objectivity, a structural reformulation of objectivity for reflexive systems—biological, cognitive, artificial, and social.Classical objectivity assumes that observation does not alter the observed system.Reflexive systems violate this assumption: their generative rule structure changes as a function of recurrence and perturbation. KOGNETIK formalizes structural self-sensitivity through the operator Ψ = ∂S/∂R where S denotes generative structure and R denotes recurrence.We show that scientific progress itself unfolds through Kognems—minimal rule mutations triggered by kognetic overload (L = 1/Ψ).Using evidence from evolutionary developmental biology and meta-learning architectures, the paper demonstrates that objectivity becomes transformation-invariance rather than state-invariance. Autological objectivity provides an epistemic foundation for systems that learn to rewrite their own rules and offers a measurable operator for next-generation reflexive AI. Intellectual Property & Licensing The KOGNETIK Research Series is released under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0). All scientific works within the series may be cited, shared, and adapted for non-commercial research purposes with proper attribution. Commercial use—including consulting, advisory services, integration into commercial platforms, monetized training, certification, or system-level deployment—is not permitted under this license and requires a separate written agreement. Full license text:https://creativecommons.org/licenses/by-nc/4.0/ For licensing, partnerships, translations, or applied development inquiries:research@kognetik.dehttps://www.kognetik.de ORCID: https://orcid.org/0009-0000-8544-4847 Kognetik Series Information KOGNETIK — Minimal Operator Definition of Reflexivity (Ψ = ∂S/∂R) Reflexivity as structural rate-of-change:Ψ = ∂S/∂R measures structural drift under recurrence. Process, not state:Reflexivity specifies a transformation rule rather than a content or level. Domain-independent operator:Applicable across biological, cognitive, artificial, social, industrial, and geophysical systems. Non-ascriptive and empirically testable:Ψ enables comparative analysis of systems via observable structure and recurrence. Higher-order phenomena as specifications:Learning, adaptation, consciousness, governance, and identity are structured regimes of Ψ.