2013
DOI: 10.1017/jlg.2013.10
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Phonology and morphology in Dutch indefinite determiner syncretism: Spatial and quantitative perspectives

Abstract: This paper uses dialect data to disentangle the contributions of phonology and morphology to the emergence of gender syncretism in the Dutch determiner paradigm. Quantitative and spatio-statistical analyses are used to identify an inverse relationship between phonological erosion and adoption of the innovative syncretic system, counter to expectation. That inverse relationship is shown to obscure the parallel development of the determiners in masculine and feminine contexts, leading to the suggestion that the … Show more

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Cited by 11 publications
(5 citation statements)
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“…Below, we provide a brief description of the workflow involved in identifying statistical "hotspots" from the raw survey data and producing the smoothed dialect maps that appear in Section 3. Similar methods of hotspot analysis have been used in earlier studies of regional patterns of phonetic (Grieve et al, 2013), morphological (Tamminga, 2013), lexical (Grieve, Speelman & Geeraerts, 2011), and syntactic (Bart, Glaser, Sibler & Weibel, 2013;Wood, 2019) variation.…”
Section: Mapping and Quantitative Analysismentioning
confidence: 96%
“…Below, we provide a brief description of the workflow involved in identifying statistical "hotspots" from the raw survey data and producing the smoothed dialect maps that appear in Section 3. Similar methods of hotspot analysis have been used in earlier studies of regional patterns of phonetic (Grieve et al, 2013), morphological (Tamminga, 2013), lexical (Grieve, Speelman & Geeraerts, 2011), and syntactic (Bart, Glaser, Sibler & Weibel, 2013;Wood, 2019) variation.…”
Section: Mapping and Quantitative Analysismentioning
confidence: 96%
“…Bungis appears to be the most common variant in the western settlements (excluding the influence of Alsatian Texas German in the southwest), while Pumpkin dominates in the eastern settlements. We can test the statistical reliability of these clusters with measures of global and local spatial autocorrelation using the Moran's I and Getis-Ord G* statistics (Grieve et al, 2011;Zanuttini et al, 2018;Tamminga, 2013).…”
Section: A Demonstration Of Spatial Autocorrelationmentioning
confidence: 99%
“…But it is not always clear whether a geographic pattern is statistically reliable. One useful tool for this is the G i * statistic (Grieve et al 2011;Tamminga 2013), referred to as a "hot spot" analysis in ArcGIS, the software we use for mapping and geospatial analysis. The hot spots test is conducted for each data point.…”
Section: Conducting Geospatial Analysismentioning
confidence: 99%