Data before models

Abstract

New theories are a constant of the now vast literature on code-mixing (CM). The Gradient Symbolic Computation model proposed by Goldrick, Putnam and Schwartz (Goldrick, Putnam & Schwartz) will appeal to many, especially those who already espouse constraint-based approaches to grammar. As variationist sociolinguists, we particularly welcome the model's incorporation of “relative probabilities of certain structures”, a feature we believe can enhance our chances of capturing actual CM behavior. We also applaud Goldrick et al.’s efforts to integrate experimental findings on co-activation with grammatical principles. Our questions concern the utility of “doubling constructions” to showcase the model, and by extension, the degree to which it can account for bilinguals’ spontaneous production of CM. A historical perspective on the field shows that none of the myriad theories of CM, often inspired by competing sets of grammatical principles, has yet achieved broad acceptance. In the absence of any widely endorsed evaluation metric – still sadly lacking -– how are we to decide amongst them?