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      Vowel harmony and positional variation in Kyrgyz

      Laboratory Phonology: Journal of the Association for Laboratory Phonology
      Ubiquity Press, Ltd.

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          Abstract

          While it is well known that the phonetic realization of a segment may differ by position, it is unclear how positional variation interacts with vowel harmony, the imperative that vowels be identical along some phonological dimension. Pearce (2008, 2012) contends that phonological harmony blocks phonetic reduction, suggesting that phonology dictates phonetic realization for this class of assimilatory patterns. This paper investigates harmony and vowel reduction in Kyrgyz, finding that non-initial vowels are more centralized than their initial-syllable counterparts. The potential sources for this reduction, including initial strengthening, supralaryngeal declination, predictability, and undershoot are discussed. The proposed predictability-based analysis provides an analysis of reduction based on phonological knowledge and representations.

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          lmerTest Package: Tests in Linear Mixed Effects Models

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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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              Multimodel Inference: Understanding AIC and BIC in Model Selection

              K. Burnham (2004)
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                Author and article information

                Journal
                Laboratory Phonology: Journal of the Association for Laboratory Phonology
                Ubiquity Press, Ltd.
                1868-6354
                1868-6354
                January 24 2020
                December 31 2020
                : 11
                : 1
                : 25
                Article
                10.5334/labphon.247
                79fd273c-7fdd-4eca-ab2e-dca663a2c6e9
                © 2020

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

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

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