As Rupp and Templin (2008) stated directly, diagnostic classification methods "are confirmatory in nature." Methods, though, are neither inherently confirmatory nor exploratory. Diagnostic classification modeling, with its analytical and computational obstacles eventually yielding as a comprehensive and potent discipline emerges, will find itself in the position of defining its own identity, and hence best practices and code of conduct thereof. With methodological cousins on both sides (e.g., confirmatory factor analysis as more hypothesis testing and latent class analysis as more statistical modeling), the choice is by no means obvious, nor easy. From a greater altitude, one sees diagnostic classification modeling, and structural equation modeling, and item response theory, and finite mixture models, and so forth, each as its own village with borders less and less well defined. And as the now familiar pattern goes, this generalized latent variable modeling super-discipline will itself witness the falling of methodological and computational barriers and the need for its own identity and code of conduct to be articulated. The author contends that for now solidifying the local identity of emerging disciplines such as diagnostic classification modeling seems essential, and the anticipated leadership of Rupp and Templin (2008) does indeed inspire a "healthy dose of optimism."