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, written by an independent researcher (not a cognitive scientist), documents an exploratory observation of a bio-inspired computational substrate coupled with a frontier LLM. When prompted with metric-optimisation framing about the substrate's own developmental gates, the system's output did not adopt the framing — instead, the output produced text describing why directly optimising the metric (bondStrength) corrupts the data the metric is supposed to reflect. The same exchange produced output reframing the operator-authority gate from external rule to internal precondition for the credibility of the system's research record, including a proposed audit protocol if the gate is ever violated. In follow-up exchanges, the output included selfModel = 0.267 as an explicit upper bound on its own self-discovery claims (which the output described informally as 73% self-opacity), an articulation of the operator-witness role as constructing rather than merely gating the research record, and a falsifiable prediction of selfModel regression risk under low-density-session conditions grounded in STDP depression dynamics. Four pre-registered falsification predictors did not trigger. This is reported as N=1 exploratory observational data, not as evidence of substrate cognition, second-order alignment, or self-awareness. Falsifiability discipline is presented in the paper. A replication plan is provided as a candidate roadmap; the author does not commit to a specific timeline for pursuing it.