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The emergence of non-canonical degree modifiers in non-standard varieties of Dutch: A constructionalization perspective

Introduction

Degree modifying adverbs have been subject to extensive linguistic discussion as they constitute a class that is very prone to language change: in studies with a (historical-) sociolinguistic perspective, the class is often portrayed as being in more or less constant flux, as initially hyperbolic new members are subject to rapid pragmatic wear-and-tear and in their turn give way to even newer members (see, e.g., Bolinger 1972; Partington 1993; Peters 1994; Paradis 2000; Lorenz 2002; Ito and Tagliamonte 2003; Macaulay 2006). While it remains to be seen whether all instances of degree modifiers are truly the result of hyperbole, what is for certain is that new members to the class are recruited from various linguistic sources. Cross-linguistically, typical source expressions for the development of new degree modifiers include words meaning ‘true’ (e.g. French vraiment, English very or truly) or ‘terrible’ (e.g. German furchtbar, English awfully), for instance, but also quantifying expressions (e.g. Italian motto, Portuguese muito, Czech velmi and Swedish mycket, all of which mean ‘very’ as well as ‘much’).

The extent to which quantifying expressions may be used to fulfil modifying functions differs widely between languages, however. In Dutch, according to Klein (1998: 31-39), expressions of high quantity do not double up as boosters, i.e. modifying adverbs which scale a property upwards: whereas prototypical low quantity expressions such as weinig ‘few’ and een beetje ‘a bit’ can function as downtoners - i.e., modifiers scaling a property downwards (e.g. Hij is weinig intelligent ‘He is not very intelligent’, Ik was een beetje dronken ‘I was a bit drunk’) - the prototypical high quantity expression veel ‘many’ cannot be used as a booster (e.g. *Ze is veel mooi ‘She is very pretty’). Instead, Dutch boosters are recruited from a variety of other lexical sources, including expressions of completeness (e.g. heel lit. ‘wholly’), modal adverbs (e.g. echt ‘really’, bepaald ‘definitely’), deictics (e.g. zo ‘so’) and, especially, qualitative adjectives (e.g. erg lit. ‘awful', knap lit. ‘handsome'/‘tight', vet lit. ‘fat', zwaar lit. ‘heavy', vreselijk lit. ‘gruesome’, ongelooflijk lit. ‘unbelievable’, verbluffend lit. ‘baffling’, etc.) (see Klein 1998: 25-62 for extensive discussion). While the above observation on veel ‘many' is correct, the generalization purported by Klein is too strong, as there

are several (admittedly, less prototypical) high quantity expressions which do seem to be developing into degree modifiers.1 Norde (2006) and De Clerck and Colleman (2013) noted the emergence of intensifying uses of the indefinite quantifier tig ‘umpteen’ in informal Netherlandic Dutch and of the quantifier noun massa’s ‘masses’ in western non-standard varieties of Belgian Dutch, respectively, see (1) and (2) for attested examples in which the items in question are used to grade qualitative adjectives. Additional instances of expressions of high quantity which double up as degree modifiers in (non-standard varieties of) present-day Dutch include duizend ‘thousand’ and een parti] ‘a set, a batch, a lot’, as illustrated in (3) and (4), respectively.[1] [2]

(1) Die van mij zijn nu 4 maanden oud, en zijn ook

those of me are now 4 months old and are too

al tig groot.

already umpteen big

‘Mine are four months old now, and they’re already real big, too.’ [www.venividivissie.org]

(2) Maar dat van die prophecy vind ik wel maar massa’s

but that of that prophecy find I prtc prtc masses

belachelijk hoor.

ridiculous PRTC

‘But I think the prophecy thing is bloody ridiculous, you know. [www.fkserv.ugent.be]

(3) Zo forum was even duizend traag.

so forum was a while thousand slow

‘So, the forum was damn slow for a while.’

[forum.scholieren.com]

(4) Hot moddefokking DAMN! Dat is me toch een partij vet, zeg!

that is me prtc a plot cool prtc

‘Hot motherfucking damn, now that’s totally cool!’

[forum.fok.nl]

The present paper offers a detailed comparison of the formal and functional properties of these four emerging modifiers, which, from a construction grammar point of view, can be seen as constituting distinct micro-level constructions (see Traugott 2008a, 2008b; Trousdale 2010). In addition to laying bare similarities and differences between these cases as different instantiations of the quantifier to degree modifier pathway of change, we will also reflect on the repercussions of the observed micro-constructional changes on higher levels of the constructional hierarchy, i.e. at the macro- and/or meso-level. We will argue that all cases are examples of grammatical constructionalization (Traugott and Trousdale 2013).

The empirical data for the investigation will be mainly drawn from online discussion forums and message boards such as the discussion forums of some 15 to 20 different Ghent University student organizations at and the Dutch forums and . These data sources are particularly suited to this kind of investigation as they contain large amounts of highly informal language, a large majority of which is contributed by people in their teens or early twenties. The examples above are pretty representative for the kind of linguistic contexts in which we typically find these emerging modifiers. By comparison, none of the modifying uses in (1) to (4) is represented in conventional corpora of written Dutch, such as the 38-million- word-corpus of the Institute for Dutch Lexicology and the CONDIV corpus, which are (mostly) made up of texts representing more formal registers of language. In addition, these corpora date back to the 1990s or ever earlier and simply fail to grasp recent developments in the class of degree modifiers. The latter drawback also applies to the Corpus of Spoken Dutch, the data for which were compiled in the period 1998-2004. What is more, even informal corpora sometimes fail to provide sufficient examples for these constructions. Constructions featuring tig as a degree modifier, for instance, are even difficult to find in gigatoken web corpora such as COW (Schafer and Bildhauer 2012). The Dutch section of this corpus contains over 2.47 billion tokens in randomly selected sentences from

1.6 million documents, yet the number of hits for tig as degree modifier in this corpus is substantially lower than the number of hits using specific Google queries (see section 2.5).[3] While the latter method does allow retrieval of a fair number of relevant constructions, one of the obvious restrictions of this approach is that data drawn from a non-restricted corpus impedes the use of advanced statistical methods (as applied to constructional changes in Hilpert 2013, for instance), nor does it allow to trace diachronic developments that underlie synchronic variation and collocational scatter.

  • [1] A note on terminology is in order here. There are in fact two subtypes of modifiers whichdenote a scale upwards from an assumed norm: next to boosters, which indicate a high pointon a scale, there are also maximizers, which denote the upper extreme of a scale (e.g. completely, utterly). Amplifiers is sometimes used as a cover term for both subclasses (e.g. in theQuirk et al. 1985 grammar). The converse of amplifiers are downtoners, which scale a propertydownwards and which, since Bolinger (1972), have been divided in three subtypes: compromisers(e.g. rather), diminishers (e.g. a little, slightly), and minimizers (e.g. barely). Intensifier and degreemodifier are two overarching terms for all subtypes of boosters and downtoners, which will beused interchangeably in this article.
  • [2] In the absence of similar modifiers deriving from quantifying expressions in English, we willoften use informal boosters such as totally, dead, damn, or bloody in the English translations.
  • [3] For example, of the collocations mentioned in Tables 4 and 5, most do not occur in COW atall, and the token frequency of the most common ones is much lower (e.g. 25 for tig meer ‘much/ many more’ or 5 for tig veel ‘very much’ as opposed to 382 and 186 in our own data set).
 
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