The paper surveys overlap between corpus linguistics and variationist sociolinguistics. Corpus linguistics is customarily defined as a methodology that bases claims about language on usage patterns in collections of naturalistic, authentic speech or text. Because this is what is typically done in variationist sociolinguistics work, I argue that variationist sociolinguists are by definition corpus linguists, though of course the reverse is not true: the variationist method entails more than merely analyzing usage data, and not all corpus analysts are interested in variation. But that being said, a considerable and arguably increasing number of corpus linguists not formally trained in variationist sociolinguistics are explicitly concerned with variation and engage in what I call corpus-based variationist linguistics (CVL). I first discuss what unites or divides work in CVL and in variationist sociolinguistics. In a plea to cross subdisciplinary boundaries, I subsequently identify three research areas where variationist sociolinguists may draw inspiration from work in CVL: conducting multi-variable research, paying more attention to probabilistic grammars, and taking more seriously the register-sensitivity of variation patterns.

This review evaluates the quality of available administrative data in the Canadian provinces, emphasizing the information needed to create integrated systems. We explicitly compare approaches to quality measurement, indicating where record linkage can and cannot substitute for more expensive record re-abstraction. Forty-nine original studies evaluating Canadian administrative data (registries, hospital abstracts, physician claims, and prescription drugs) are summarized in a structured manner. Registries, hospital abstracts, and physician files appear to be generally of satisfactory quality, though much work remains to be done. Data quality did not vary systematically among provinces. Primary data collection to check place of residence and longitudinal follow-up in provincial registries is needed. Promising initial checks of pharmaceutical data should be expanded. Because record linkage studies were “conservative” in reporting reliability, the reduction of time-consuming record re-abstraction appears feasible in many cases. Finally, expanding the scope of administrative data to study health, as well as health care, seems possible for some chronic conditions. The research potential of the information-rich environments being created highlights the importance of data quality.