["Transactions in GIS, Volume 30, Issue 4, June 2026. ", "\nABSTRACT\nGoodchild and Li's claim that replication in spatial social and environmental sciences “must be weak” highlights the tension between generalization and place‐based heterogeneity. This paper accepts their descriptive insight, that is, exact replication across space and time is often unrealistic, but interrogates the normative implications of labeling it “weak.” While not advocating poor science, this framing risks being interpreted as a relaxation of evidentiary standards, particularly under increasing pressure to publish context‐specific findings, potentially eroding rigor at the margin. In response, I propose a constructive alternative: conditional generalization, which treats spatial variation as a central object of inquiry by explicitly modeling how parameters depend on measurable place attributes. This shifts the focus from whether results replicate identically to how and why they vary across contexts. Drawing on the National Academies' taxonomy of reproducibility and replicability, and advances in spatial causal inference, hierarchical modeling, spatially explicit machine learning, and transportability theory, I outline four methodological pathways to operationalize this approach. The goal is not to reject place‐based science, but to reconcile strong evidentiary standards with spatial heterogeneity, transforming variability from a limitation into a structured, testable, and generalizable form of knowledge.\n"]