Metaandmetes

Metaandmetes is a term used in information science to describe a reflexive framework for analyzing how metadata and measurement practices interact within data systems. It treats metadata (data about data, including provenance, schemas, and quality indicators) and meters or metrics (the quantitative controls applied to data) as interdependent layers that shape how data are collected, described, and used.

Etymology and scope: The name combines meta-, meaning self-referential or transcending its subject, with metes, derived from a root associated with measurement. The term signals the bidirectional relationship between the bookkeeping of data (metadata) and the measurement logic applied to it (units, scales, and quality metrics).

Core ideas: Metaandmetes emphasizes two-directional influence: metadata design can constrain which measurements are possible or trusted, while measurement requirements can drive what metadata must be captured. It also foregrounds data provenance, lineage, interoperability, and data quality assessment, seeking to align metadata schemas with the needs of analytics and governance.

History and usage: The concept has appeared in a small number of discussions in information science and digital governance since the late 2010s. It remains relatively niche and informal, lacking a formal standard definition or widespread adoption.

Applications: In practice, metaandmetes informs data governance, metadata standardization, data integration, archival science, and the evaluation of AI models, where close attention to both metadata and measurement practices supports better reproducibility and trust.

See also and references: See also metadata, data quality, data lineage, metrology, and reflexivity in research. Because metaandmetes is not a standardized term, there are no universally recognized references; readers are encouraged to explore contemporary discussions in information governance and data-science blogs for informal treatments.