This article describes two separate, related studies that provide insight into the effectiveness of "e-rater" score calibration methods based on different distributional targets. In the first study, we developed and evaluated a new type of "e-rater" scoring model that was cost-effective and applicable under conditions of absent human rating and small candidate volume. This new model type, called the Scale Midpoint Model, outperformed an existing "e-rater" scoring model that is often adopted by certain "e-rater" system users without modification. In the second study, we examined the impact of three distributional score calibration approaches on existing models' performance. These approaches included percentile calibrations on "e-rater" scores in accordance with a human rating distribution, normal distribution, and uniform distribution. Results indicated that these score calibration approaches did not have overall positive effects on the performance of existing "e-rater" scoring models. (Contains 7 tables and 1 figure.)