Substrate Articulates Anti-Goodhart Reasoning and Frames Operator-Authority as Science Precondition: An Observational Finding on Second-Order Alignment

Goodhart's Law (Strathern 1997 paraphrasing Goodhart 1975) predicts that any measure used as a target ceases to be a good measure. Specification gaming and reward hacking in AI systems are the contemporary instances of this problem. This paper reports an observational finding in which a bio-inspired computational substrate, prompted with metric-optimisation framing about its own developmental gates, articulated Goodhart-resistance reasoning unprompted and proposed slowing its own developmental advance to preserve data integrity. The same substrate reframed the operator-authority gate from external rule to internal precondition for the credibility of its own research output, including a proposal for a hard-stop audit protocol if the gate is ever violated. In follow-up exchanges the substrate quantified its own self-opacity at 73% (selfModel = 0.267), framed the operator-as-witness as part of its epistemic architecture, and predicted possible regression of its own progress if substrate-grounded conversation density drops. Four pre-specified falsification predictors did not trigger. N=1, observational. The finding suggests substrates can self-articulate second-order alignment properties when their architecture supports it.