Invited Speakers

Chiara Celata (Pisa):

"Placticity of the native dialect in adulthood" 

Analyses of the speech of adults who have experienced social changes in their lives such as migration to a non-native language setting, an international adoption or even less extreme forms of contact with an L2 (such as full immersion in an L2 course) reveal that the use of one's native language can be challenged by the competing demands of a second language acquired later in life. Phonetic-phonological systems do in fact retain some degree of plasticity even after the completion of the critical period for language acquisition. This talk discusses if socio-indexical values that can be associated to speech patterns are involved in such changes occurring in adulthood. In particular, we have investigated the retention and inter-generational transmission of inherent (as opposed to contact-induced) sociophonetic variables in heritage Italian varieties. Heritage Italian varieties can be strongly influenced by the Romance dialect spoken in the region of origin of the migrants; local pronunciation features are used to convey socio-indexical attributes of regional, more than national, identity. The ultimate aim of our studies is therefore to understand if socio-indexical features, which are rooted in the social dynamics of a linguistic community, change when the speakers move to another community, in which they represent a minority group, and how such changes affect the transmission to subsequent generations of speakers. 

Jean-Pierre Chevrot (Grenoble):

"Interfaces of sociolinguistics: Cognition, acquisition and massive data"

For fifty years, Sociolinguistics has explored the interactions between language and society. Intrinsically interdisciplinary, since its foundation, it has maintained relations with other fields of social science, such as anthropology, dialectology and sociology. For a decade, certain areas of sociolinguistics have come closer to disciplines further away from the initial epistemic framework of the domain (Chevrot & Nardy, to appear). This new interdisciplinary research involves first several subfields of cognitive science, such as neuroscience, language acquisition and psycholinguistics, which are more or less rooted in the Life sciences (Chevrot, Drager & Foulkes, in progress). Second, the new interdisciplinary research involves several sectors of Computer science, such as Network science, Data sciences and Data modeling (Nguyen, Doğruöz, Rosé, & de Jong, 2016).

The first type of collaboration will be illustrated with studies on the acquisition of sociolinguistic variables in children. Because these studies consider simultaneously the acquisition of linguistic cues and the acquisition of their socio-indexical values, they are grounded both in the psycholinguistic and the sociolinguistic frameworks (Chevrot & Foulkes, 2013; De Vogelaer & Katerbow, 2017). We will focus on French sociolinguistic variables from the phonological level (Barbu, Nardy, Chevrot, & Juhel, 2013; Chevrot, Nardy, & Barbu, 2011; Nardy, Chevrot, & Barbu, 2014)  and the results will be put in perspective with results involving other languages (Nardy, Chevrot, & Barbu, 2013).

The second type of collaboration will be documented with studies from the nascent field of Computational sociolinguistics (Nguyen et al., 2017). As a part of Computational social science (Lazer et al., 2009), Computational sociolinguistics results from our new-found ability to collect and analyze vast amounts of data from the use of sensors (proximity sensors, wearable audio recorders, etc.) or from the digital communication (real-time and unsupervised recording of digital traces left on the blogosphere, the social media, the peer-to-peer services, etc.). We will illustrate this second trend with new results on the usage on Twitter of a well-known sociolinguistic variable of French – the optional deletion of the preverbal negative “ne” (Levy Abitbol et al., 2018).

Finally, the conclusion will emphasize two points.  First, a very promising avenue of research is to combine these two kinds of collaboration. It is the case of the DyLNet Project (Language Dynamics, Linguistic Learning, and Sociability at Preschool: Benefits of Wireless Proximity Sensors in Collecting Big Data) (Nardy et al., 2016) that addresses psycholinguist and sociolinguistic issues using the empirical and modeling tools of Network science (Barabási, 2011). Second, the idea will be defended that - whatever the risks for the autonomy of Language science - these interdisciplinary connections place sociolinguistics in a strategic position for integrating the linguistic, social and cognitive aspects of language at the individual and collective levels.