Accurate models of material behavior are essential for predictive simulation of critical quantities of interest (QoIs) for engineering application. However, given the rigid approximations of material physics in most available models, there is typically some model form error that cannot be fully eliminated. This error can influence simulation results in unpredictable and profound ways. It is common practice to calibrate material models by seeking a set of parameter values that are optimal in the global sense that the calibrated model best fits the available data. The canonical example is calibration to minimize the sum of squared errors between the model output and some set of observations. In this paradigm, the calibrated model is determined solely by the data and is transferable from one analysis to the next. However, contrary to the underlying assumption that there exists a single, context-independent optimal model calibration, engineering practice clearly demonstrates that different model calibrations (even given the same data) can be optimal for different end-use scenarios. Here, we present a method of material model calibration that redistributes model form error to minimize the effects of this error on the prediction of a particular QoI. The parameter values resulting from this method are context-specific, in the sense that they minimize the expected error for a given analysis rather than minimizing the expected error over the broad range of scenarios represented by the calibration data. In this paradigm, it is the approach that is transferable between contexts, rather than the calibrated parameter values. We demonstrate the efficacy of this framework with a simple but relevant problem in elasto-plasticity. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology \& Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.