法语provenalal中的意外分词*

IF 0.4 3区 文学 0 LANGUAGE & LINGUISTICS
E. Stark, Jan Pavel Davatz
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引用次数: 2

摘要

这篇文章旨在增加我们对法国、瑞士和意大利使用的一种高度濒危语言的“分词”(如法语元素du in Je bois du vin,“我喝(一些)葡萄酒”)的知识,即法语普罗旺斯语。根据最近来自奥斯塔山谷的实地调查数据(2017年)和ALAVAL地图集项目的数据,我们将从地理和句法的角度讨论异常值,即“分词”在它们不应该出现的地方的出现。这可能是因为根据现有文献,这些区域及其各自的变体并没有以它们为特征,也可能是因为从标准法语中得知的句法规律不承认它们(在否定范围内,在量化表达之后)。我们将尝试对这些意想不到的元素的存在提出一些解释,包括内部和外部,即通过语言接触,并更普遍地主张对野外工作数据中的稀有元素进行更系统的考虑,这些元素不应该太容易被“解释掉”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unexpected Partitive Articles in Francoprovençal*
This contribution seeks to add to our knowledge on ‘partitive articles’ (like the French element du in Je bois du vin, ‘I drink (some) wine’) in a highly endangered language spoken in France, Switzerland and Italy, namely Francoprovenc al. Based on recent fieldwork data (2017) from the Aosta Valley and data from the ALAVAL atlas project, we will discuss outliers in a geographical and syntactic perspective, i.e. occurrences of ‘partitive articles’ where they should not appear. This either because the regions and their respective varieties do not feature them according to the existing literature or because syntactic regularities known from Standard French do not admit them (under the scope of negation and after quantifying expressions). We will try to propose some explanations for the existence of these unexpected elements, both internal and external, i.e. via language contact, and argue more generally in favour of a more systematic consideration of rare elements in fieldwork data, which should not too readily be “explained away”.
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来源期刊
STUDIA LINGUISTICA
STUDIA LINGUISTICA LANGUAGE & LINGUISTICS-
CiteScore
1.20
自引率
0.00%
发文量
21
期刊介绍: Studia Linguistica is committed to the publication of high quality, original papers and provides an international forum for the discussion of theoretical linguistic research, primarily within the fields of grammar, cognitive semantics and language typology. The principal aim is to open a channel of communication between researchers operating in traditionally diverse fields while continuing to focus on natural language data.
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