Calibration weighting is a methodology under which probability-sample weights are adjusted in such a way that when applied to survey data they can produce model-unbiased estimators for a number of different target variables. This paper briefly reviews the history of calibration weighting before the term was coined and some major developments since then. A change in the definition of a calibration estimator is recommended. This change expands the class to include such special cases as, 1, randomization-optimal estimators, and, 2, randomization-consistent estimators incorporating local polynomial regression. Although originally developed as a method for reducing sampling errors, calibration weighting has also been applied to adjust for unit nonresponse and for coverage errors. A variant of the jackknife variance estimator proposed here should prove computationally convenient for these applications.