Levshina, Namboodiripad, et al. preprint Why we need a gradient approach to word order Abstract This paper argues for a gradient approach to word order, which treats word order preferences, both within and across languages, as a continuous variable. Word order variability should be regarded as a basic assumption, rather than as something exceptional. Although this approach follows naturally from the emergentist usage-based view of language, we argue that it can be beneficial for all frameworks and linguistic domains, including language acquisition, processing, typology, language contact, language evolution and change, variationist linguistics and formal approaches. Gradient approaches have been very fruitful in some domains, such as language processing, but their potential is not fully realized yet. This may be due to practical reasons. We discuss the most pressing methodological challenges in corpus-based and experimental research of word order and propose some solutions and best practices. Keywords word order, gradience, continuous variables, entropy, typology, variability Main text 1 Our motivation for writing this paper In this paper we argue for a gradient approach to word order. A gradient approach means that word order patterns should be treated as a continuous variable. For example, instead of labelling a language as SO (subject-object) or OS (object-subject), we can compute and report the proportion of SO and OS based on behavioural data (corpora and experiments). Similarly, instead of speaking of fixed, flexible, or free word order, we measure the degrees of this variability by using quantitative measures, such as entropy. Instead of simply saying that word order in a language provides a cue for inferring Subject and Object, we will measure the reliability and strength of this cue based on behavioural data. Note that our claims apply to the order of words and larger-than-word units, as well, such as syntactic constituents and clauses; in this paper, we use “word” as a general term, and specify constituents/clauses when relevant.1 1 We recognize that the term “word order” is not a very fortunate one. First of all, the term “word” is a problematic comparative concept for cross-linguistic studies (Haspelmath, 2011). Second, “word order” is not very specific, 1 Levshina, Namboodiripad, et al. preprint A gradient approach can be illustrated by a simple analogy: Instead of using categorical colour terms like “white”, “blue” or “orange”, we can encode them numerically by using different codes. For example, we can talk about a color for which there is a word in English, “orange”, in RBG terms as being 100% red, 64.7% green, and 0% blue. In CMYK color space, it is 0% cyan, 35.3% magenta, 100% yellow and 0% black, and it is assigned a hex code #ffa500. However, an additional advantage of this continuous measure is that we can also describe a color for which there isn’t a standard label, such as #BA55D3, which is 73% red, 33% green, and 83% blue, and is given the label “medium orchid” in the X11 color names system. Thus, using various continuous measures, we can not only account for the underlyingly gradient properties of color, but we can also describe more and less prototypical colors on equal footing. For an analogy within linguistics, we can compare categorical notions such as “flexible” and “rigid” to apparent categorical notions such as “voiced” and “voiceless” in phonology. Categorizing consonants as being “voiced” or “voiceless” has been useful for describing many phenomena, and, while linguists had been using this distinction for decades, many had noted that this categorization was not sufficient for accounting for stops in many languages, including English. For example, Lisker and Abramson (1964) showed that what appeared to be a categorical distinction (voiced/voiceless or fortis/lenis, etc.) was in fact better described using an objective continuous measure, that of Voice Onset Time. The notion of Voice Onset Time has both birthed and specified theoretical approaches to perception, production, change, and variation in this empirical domain, and stimulated many research projects (Raphael 2004). We advocate for an analogous theoretical move in the domain of syntax, word and constituent order more specifically. Despite some previous research problematizing categorical distinctions in linguistics (e.g., Wälchli 2009), non-gradient approaches to word order are still hegemonic (e.g., Dunn et al. 2011; Dryer 2013; Hammarström 2015). There have been theoretical and practical reasons for preferring them. From the theoretical perspective, the predominant view has been that grammar in general and word order in particular should be described by discrete features, categories, rules, or parameters (e.g., Jackendoff 1971; Vennemann and Harlow 1977; Lightfoot 1982; Guardiano and Longobardi 2005). A practical reason is that it is still difficult to obtain behavioural or corpus data in many languages, which would allow for analyses of gradience. Yet, the situation is changing. First of all, there is a growing interest in probabilistic gradient phenomena, from “soft constraints” (Bresnan et al. 2001) to “probabilistic linguistics” (Bod et al. 2003) or “probabilistic grammar” (Grafmiller et al. 2018), which are captured by complex statistical models of language users’ behaviour (e.g., Gries, 2003; Bresnan et al. 2007; Szmrecsanyi et al. 2016, to name just a few). In contrast to traditional grammar with a clear-cut division between semantics, syntax, morphology and other domains, cognitive and usage-based linguistics has argued for a continuum between lexicon and grammar (Langacker 1987; Croft 2001). Morphological decomposition of complex words is also a matter of degree, e.g. refurbish is more decomposable than rekindle, which is perceived more holistically (Hay 2001; Bybee since it can represent the order of constituents, elements in a nominal phrase, or thematic roles. In addition, we can also investigate the order of topic and comment, given and new, etc. (cf. Mithun, 1992). 2 Levshina, Namboodiripad, et al. preprint 2010). The usage-based perspective on language inherently implies gradience, because linguistic forms, meanings, and their pairings are organized in networks linked by taxonomic, sequential, and symbolic associations, and the strength of these associations can vary (Diessel 2019). Word order gradience follows naturally from such approaches, which assume that grammar is emergent at its core. For example, English is mostly a prepositional language. Since many prepositions represent a result of grammaticalization of verbs, and English has predominantly VO order, the normal outcome of this process is development of prepositions. However, English also has a few postpositions, such as ago and notwithstanding. Nouns that are now used with those postpositions were originally the subjects of the source verbs (Dryer 2019).2 The bottom-up emergence of grammar from language use thus is very likely to result in variability/construction-specific idiosyncrasies, such that a categorial approach of applying labels to languages as a whole is less accurate than a gradient approach, which can quantify degrees of divergence from some canonical extreme. In addition to the change in the theoretical frameworks, there are also good practical reasons for adopting a gradient perspective. Now we have access to new data sources in the form of large corpora, as well as software for processing and analyzing them statistically. The recent quantitative turn in linguistics (Janda 2013; Kortmann 2020), which has been possible thanks to the development of user-friendly statistical software and methods, has made us better equipped than ever for investigating gradient phenomena. In this paper we argue that a gradient approach is more descriptively adequate than the traditional approach. Traditionally, word order is described with the help of categorical labels like “rigid/fixed”, “flexible”, and “dominant”.3 Rigid order means that some orders are “either ungrammatical or used relatively infrequently and only in special pragmatic contexts” (Dryer 2013). If different possible word orders are grammatical, languages have flexible order. Many flexible order languages are claimed to have a dominant word order, i.e. the more frequent one (e.g., the order that is used at least twice as frequent as any other order is considered dominant);4 in the absence of frequency data, or in languages where the dominant order might differ based on contexts such as register or genre (see Payne 1992), this could also be the order labeled as “basic”, “pragmatically neutral” or “canonical” in language descriptions. However, a clear-cut distinction between rigid order languages and flexible order languages with a dominant order is problematic. First of all, pragmatic neutrality is a slippery notion, which depends on the specific situation. In spontaneous informal conversations in Russian, for example, there is nothing pragmatically marked about putting a newsworthy object first. For example, (1a) from the transcribed spoken text, has OSV.5 Compare it with (1b) taken 2 More exactly, ago developed from an obsolete verb meaning “pass”, whereas notwithstanding developed from notwithstand meaning “provide an obstacle for”. 3 Since the use of different orders in a flexible language is pragmatically motivated by information flow (e.g., Mithun 1992), syntactic domain minimization (Hawkins 1994), disambiguation pressures (Bouma 2011) and other factors, a categorical notion such as “free”