The prevailing data-driven paradigm in AI has largely neglected the generative nature of data. All data, whether observational or experimental, are produced under specific conditions, yet current approaches treat them as context-free artifacts. This neglect results in uneven data quality, limited interpretability, and fragility when models face novel scenarios. Evaluatology reframes evaluation as the process of inferring the influence of an evaluated object on the affected factors and attributing the evaluation outcome to specific ones. Among these factors, a minimal set of indispensable elements determines how changes in conditions propagate to outcomes. This essential set constitutes the evaluation conditions. Together, the evaluated object and its evaluation conditions form a self-contained evaluation system — a structured unit that anchors evaluation to its essential context. We propose an evaluatology-based paradigm, which spans the entire AI lifecycle — from data generation to training and evaluation. Within each self-contained evaluation system, data are generated and distilled into their invariant informational structures. These distilled forms are abstracted into reusable causal-chain schemas, which can be instantiated as training examples. By explicitly situating every learning instance within such condition-aware systems, evaluation is transformed from a passive, post-hoc procedure into an active driver of model development. This evaluation-based paradigm enables the construction of causal training data that are interpretable, traceable, and reusable, while reducing reliance on large-scale, unstructured datasets. This paves the way toward scalable, transparent, and epistemically grounded AI.
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