Measurement claims carry high stakes, yet students and early-career professionals rarely receive explicit training in the reasoning chain that justifies them. Despite growing emphasis on transferable competence, measurement reasoning remains a blind spot: learners seldom map a measurand (the quantity of interest) to an estimand (the reported quantity inferred from data), anchor inference in an explicit noise or likelihood model, or report results with uncertainty and traceability. This fragmentation weakens rigor and communication across domains. We define cross-disciplinary measurement literacy as the ability to connect a measurand to an estimand, justify inference under explicit noise and likelihood models, and report results with uncertainty and traceability. We operationalized this construct through a unified $\mathrm{Encoding}\to \mathrm{Evolution}\to \mathrm{Readout}\to \mathrm{Estimation}$ schema integrated with metrological closure and implemented it in a $4\times 2\text{−}\mathrm{h}$ module. This multicohort descriptive evaluation includes five cohorts with verified pre- and postadministration records ($N=114$) and two undergraduate team-task cohorts. Cohort-level summaries showed immediate post-test means 34–54 points above pretest means and normalized gains of 0.642–0.782 on an intervention-aligned assessment. Team rubric summaries provided evidence of feasible group-level enactment of the schema, while uncertainty budgeting and traceability remained the principal difficulties. This study is designed as a bounded descriptive evaluation. It provides evidence for the schema’s teachability and its utility as a short-term organizer for measurement reasoning, specifically within the instructional context studied.

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• Epistemology, attitudes, & beliefs
• Instructional materials development
• Instructional strategies
• Learning theory
• Quantum metrology
• Research methodology
• Scientific reasoning & problem solving