Exploring epicentres empirically

Abstract

The present paper studies the dative alternation with GIVE, i.e. the alternation between the double-object construction (e.g.John gave Mary a book) and the prepositional dative (e.g.John gave a book to Mary), in relation to the norms underlying this constructional choice in six South Asian Englishes. ViaMultifactorial Prediction and Deviation Analysis with Regression(MuPDAR) including random effects, we identify (i) factors triggering different constructional choices in South Asian Englishes in comparison to British English and (ii) the linguistic epicentre of English in South Asia with regard to the dative alternation. We are able to show that discourse accessibility of patient and recipient as well as pronominality of recipient are actuators of structural nativisation in South Asian Englishes and — in agreement with a more general sociolinguistic approach — find via a bottom-up approach that Indian English may be regarded as the linguistic epicentre of English for South Asia.

Empirical perspectives on two potential epicenters: The genitive alternation in Asian Englishes

“…the alternation between the double-object construction as in John gave Mary a book and the prepositional dative as in John gave a book to Mary -highlighted that IndE provided the best model for the factor-guided mostly subconscious selection process of either of the two constructions in the remaining five South Asian Englishes (cf. Gries and Bernaisch 2016). Still, given that claims of epicentral influence should be substantiated with multiple examples (cf .…”

“…Following the above work, Gries and Bernaisch (2016) were the first to apply this approach, which was initially only applied in LCR settings, to IV research by looking at how the dative alternation with GIVE is used differently in BrE compared to six indigenized varieties. In the current study, we also apply the general logic of this approach to our genitive data, but, following Deshors and Gries (2016), use two random forests instead.…”

“…On the one hand, random forests do not provide all the 'machinery' and results that a regression analysis can provide in the best of cases. For instance, Gries and Bernaisch's (2016) regression analyses are multilevel/mixed-effects models that are instructive in how they can include random effects such as the hierarchical structure of the corpus. In addition, some kinds of effects are probably easier to explore with regressions than with random forests -e.g., slopes of polynomial to a degree of 2 or greater.…”

“…As described in Section 2.2 above, the general logic follows that of Gries and Bernaisch (2016): for each variety V x of the six varieties V 1-6 ,…”

“…Unlike in Gries and Bernaisch (2016), we then analyzed these data in two ways: first, we computed a cluster analysis to determine degrees of similarity between varieties and to see which, if any subgroups, emerge; second, we tested which South Asian and which Southeast Asian variety predicted the others in those groups best to see what that would reveal about each variety's potential epicenter status.…”