First-order minimaxity offers limited guidance for choosing among global-local shrinkage priors, since different local-tail distributions can yield the same leading risk. Comparisons at a common global scale may also be misleading, as the scale interacts with the shape of the local prior. We study the worst-case squared-error risks of posterior means in sparse Gaussian normal-means estimation, allowing each prior its own risk-minimizing global scale. For local-variance priors with polynomial tails and logarithmic corrections, we characterize the calibrations that retain first- and second-order minimaxity and derive the asymptotic form of the unrestricted oracle scale. Even after this optimization, differences in risk remain. The polynomial tail index, logarithmic tail exponent and a scale-invariant higher-order correction distinguish priors at successively finer orders. In particular, the horseshoe has smaller asymptotic oracle risk than the horseshoe+, despite the latter's additional logarithmic tail factor. We also establish the calibration accuracy needed to preserve these differences and sufficient conditions for their stability under high-dimensional Gaussian regression designs.

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