Conclusion

Organizational Correspondence cannot become an empirical research program without measurement. But it cannot be measured responsibly by reducing it to a score, maturity level, graph statistic, trace count, semantic similarity value, dashboard signal, or AI-generated judgment. The construct is relational, multi-level, temporal, evidential, and often contested. Its measurement must preserve those characteristics.

This paper has developed a conceptual measurement framework for Organizational Correspondence in AI-native organizations. The framework specifies the measurement object, construct architecture, level and unit system, evidence model, metric and indicator architecture, profile-first aggregation principles, temporal logic, and methodological assurance requirements. It distinguishes measurement from assessment, evaluation, diagnosis, recommendation, and product instrumentation. It positions Correspondence Measurement as a bridge between conceptual theory and future empirical validation.

The central argument is straightforward: Organizational Correspondence can be measured, but only if the measurement remains faithful to the theory it operationalizes. A correspondence measure is not a number alone. It is a traceable, evidence-based, uncertainty-aware, context-bound representation of a relationship among organizational representations. That is what makes measurement scientifically useful, organizationally responsible, and theoretically consistent with the broader Correspondence Theory program.

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