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      Exploring individual variation in Turkish heritage speakers’ complex linguistic productions: Evidence from discourse markers

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      Applied Psycholinguistics
      Cambridge University Press (CUP)

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          Abstract

          Research on multilingual speakers is often compared to monolingual baselines which are commonly treated as if they were homogeneous across speakers. Despite recent research showing that this homogeneity does not hold, these practices reproduce native-speakerism and monolingualism. Heritage language research, which established itself in the past two decades, is no exemption. Focusing on three predefined linguistic groups, namely Turkish speakers which are framed as monolingual in Turkey as well as two heritage bilingually framed groups in Germany and the USA, we ask: (1) Do heritage speakers of Turkish produce more discourse and fluency markers (FMs) than monolingual speakers? (2) Are the groups homogeneous, or is there wide variation between speakers across groups? We focus on the variation between and within groups using Bayesian Linear Regression with a multilevel model for speakers and heritage groups. Our findings confirm that the use of discourse and FMs is largely defined through individual variation, and not through the belonging to a certain speaker group. By focusing on variation across groups rather than between groups, our study design supports the growing body of literature that questions common heritage language research practices of today and shows alternative paths to understanding heritage grammars.

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            brms: An R Package for Bayesian Multilevel Models Using Stan

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              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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                Author and article information

                Contributors
                Journal
                Applied Psycholinguistics
                Applied Psycholinguistics
                Cambridge University Press (CUP)
                0142-7164
                1469-1817
                July 2023
                May 19 2023
                July 2023
                : 44
                : 4
                : 534-564
                Article
                10.1017/S0142716423000267
                abe6031f-b672-4f60-95a5-cf0f9f62d3be
                © 2023

                Free to read

                http://creativecommons.org/licenses/by/4.0/

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