Democratization and gender-neutrality in English(es)

"Democratization" and "gender-neutrality" are two concepts commonly used in recent studies on language variation. While both concepts link linguistic phenomena to sociocultural changes, the extent to which they overlap and/or interact has not been studied in detail. In particular, not much is known about how linguistic changes related to democratization and gender-neutrality spread across registers or varieties of English, as well as whether speakers are aware of the changes that are taking place. In this paper we review the main theoretical issues regarding these concepts and relate them to the main findings in the articles in this issue, all of which study lexical and grammatical variation from a corpus-based perspective. Taken together, they help unveil some of the conscious and unconscious mechanisms that operate at the interface between democratization and gender-neutrality.Peer reviewe

Deconstructing variation in pragmatic function: a transdisciplinary case study

Despite recent advances (e.g. Cheshire 2007; Pichler 2010; Denis 2015), discourse-pragmatic variables continue to challenge variationist theory and methods. An overarching dilemma concerns multifunctionality, raising difficulties for semantic equivalency and the circumscription of the variable context. In this article we present a case study to illustrate that deconstructing a discourse-pragmatic marker into its composite parts reveals clear criteria for disambiguating its principal function and its contextually derived functions. The discussion centres on the pragmatic marker eh in Canadian English. We illustrate that its multifunctionality is derivable from four parts: principal function, syntactic context, prosodic context, and discourse context. Our deconstruction uses a two-pronged methodology, drawing on storyboard elicitation and sociolinguistic interview data, which mutually reinforce our theoretical arguments. Under this transdisciplinary lens, the exponents of form and function become predictable, constrainable, and systematically derivable for probabilistic modelling within and across speech communities