This research release examines the critical distinction between computational uniqueness and observational identification in complex modeling. It argues that software often produces a single result not because the data requires it, but because hidden selectors—such as priors, constraints, or algorithmic rules—narrow down multiple valid possibilities. By comparing cases in epidemiology, economics, and climate science, the authors demonstrate how these underlying assumptions are frequently misattributed to empirical evidence. To address this, they propose a Selector Ledger and a standardized audit framework to help researchers transparently document which parts of a conclusion stem from raw data versus subjective restrictions. The ultimate goal is to ensure that scientific claims accurately reflect the limitations of the information available, preventing evidential overreach in high-stakes decision-making.