metadequate

Metadequate is a recently coined term used to describe adequacy at a meta level—the sufficiency of the criteria, frameworks, or standards that govern evaluation, analysis, or design. The word combines meta- with adequate to signal reflexivity: not only is a system or claim adequate by its own criteria, but its criteria themselves are judged to be adequate.

In philosophy and critical theory, metadequacy refers to the sufficiency of meta-criteria used to assess justification, knowledge claims, or theoretical frameworks, emphasizing reflexivity and the legitimacy of the evaluative process. In practice, it asks whether the standards by which a claim is judged are themselves sound and comprehensive.

In software engineering and data science, a metadequate approach evaluates not only outcomes but the evaluation process itself, such as meta-testing that ensures tests, metrics, and benchmarks cover relevant edge cases, fairness, robustness, and reproducibility. This broader scrutiny aims to reduce overlooked biases or gaps in assessment.

In machine learning, metadequacy may describe models that satisfy meta-criteria—such as fairness, interpretability, privacy, and generalizability—in addition to predictive accuracy, aligning model evaluation with broader ethical and practical considerations.

Etymology and usage: the term is a portmanteau of meta- and adequate; it has appeared in niche discussions and informal glossaries, with no widely adopted definition. It remains an umbrella concept for discussions about the adequacy of evaluative criteria themselves rather than a fixed technical standard.

Examples: a metadequate benchmark would incorporate bias assessment and harm potential, while a metadequate methodology would require reflexive evaluation of the criteria used to judge outcomes.

See also: meta-evaluation, reflexivity, meta-analysis.