  Off-the-shelf models are widely used by computational social science
researchers to measure properties of text, such as sentiment. However, without
access to source data it is difficult to account for domain shift, which
represents a threat to validity. Here, we treat domain adaptation as a modular
process that involves separate model producers and model consumers, and show
how they can independently cooperate to facilitate more accurate measurements
of text. We introduce two lightweight techniques for this scenario, and
demonstrate that they reliably increase out-of-domain accuracy on four
multi-domain text classification datasets when used with linear and contextual
embedding models. We conclude with recommendations for model producers and
consumers, and release models and replication code to accompany this paper.
