5 Discussion

I will now put the quantitative findings above into the context of existing studies. First, the rise of VO in Middle English can be measured in prose and poetry. Both sources of data are therefore useful as evidence. However, the change proceeds in a markedly different manner in prose and poetry. The latter genre is considerably more conservative (replicating Foster & van der Wurff Reference Foster and van der Wurff1995, Reference Foster and van der Wurff1997) and also innovates the new order more slowly (a new finding in this explicit form).

One could attempt to establish a typology of syntactic features that are more or less prone to sustained variation in poetry. Shorter, morphosyntactic features may be harder to manipulate consciously whereas longer, macrostructural patterns are easily exploited for poetic effect. This assumption could begin to explain why there is no marked genre-specific differential in the rise of when as a subordinator (Zimmermann Reference Zimmermann2020) or the development of Jespersen's Cycle (Truswell et al. Reference Truswell, Alcorn, Donaldson and Wallenberg2019), whereas the loss of null subjects (Walkden & Rusten Reference Walkden and Rusten2017) or the change in the relative order of verb and object clearly proceed differently between prose and poetry.

A related perspective on the diachronic genre differences is offered by the notion of ‘poetic function’ (Jakobson Reference Jakobson1960), poetry's focus on message formulation and linguistic creativity, utilising archaic features as readily as colloquial ones for aesthetic effect (e.g. Semino Reference Semino2002). Most importantly, conservative OV patterns may persist to accommodate rhymes on the verb, although other artistic uses are conceivable (e.g. Fisher Reference Fischer1992: 373). These poetic functions may obfuscate the true rate of VO order associated with a given time, thus adding noise not encountered in the prose. Indeed, the variance of deviance residualsFootnote ⁹ of the model for texts before 1400 indicates larger between-text variability and poorer predictivity for poetry (1.29) than for prose (0.43).

One can also think of the findings in terms of ‘archaisation’, a concept by which I mean a gradual divergence in the frequency of use of a linguistic feature between idiomatic usage, where the decline is faster, relative to a more marked genre, in which the loss is slower, leading to the feature's association with that genre and its eventual niche survival. Here, prose is closer to spoken language and loses VO more quickly, while poetry is less natural and abandons VO more slowly, so that OV patterns are re-evaluated as distinctly ‘poetic’.Footnote ¹⁰ The concept can be applied to a wide range of phenomena, e.g. the restriction of be come or thou to hyper-formal or biblical registers.

It bears repeating that VO order reaches a probability of more than 99 per cent of use in prose in the fifteenth century. VO should thus be regarded as the norm from that time on (pace some authors who sketch the time course differently, e.g. ‘the VO base was dominant by 1200’ (Kiparsky Reference Kiparsky1996: 144Footnote ¹¹), ‘the change took place … through roughly the late 17th c.’ (Wallenberg et al. Reference Wallenberg, Bailes, Cuskley and Ingason2021: 3); see the excellent summary in Fischer et al. 2000: ch. 5). In fact, the data presented here allow a refinement of a plausible end point of the change in prose, datable to the early to mid fifteenth century.

The weight variable is implicated in a likely reason for the emergence of VO. The earliest medieval English is sometimes regarded as underlying OV plus a right-ward extraposition process (e.g. Lightfoot Reference Lightfoot1979; Kemenade Reference Kemenade1987). This early extraposition is mainly driven by weight. Indeed, the ancient poem Beowulf shows post-verbal objects predominantly with heavy objects / across metrical breaks (Pintzuk & Kroch Reference Pintzuk and Kroch1989; Taylor Reference Taylor2005). Subsequently, lighter objects begin to appear post-verbally and eventually a new underlying VO base is said to come about as a result. It would in principle be possible to derive a testable prediction from this account: if weight and time are merely additive, VO orders would emerge in addition to and independently of weight constraints. If on the other hand there is a significant interaction effect between weight and time such that VO rises faster with light objects, the new word order could be interpreted as feeding on extraposed objects, which would support a causal association. As stated above, the data are unfortunately too sparse and too noisy to ascertain with reasonable certainty if such an interaction effect should be postulated.

Ever since Lightfoot's (Reference Lightfoot1991) proposal for a learning bias leading children to acquire linguistic structures predominantly from unembedded clauses (degree-0 learnability), main clauses have been seen to be overall more innovative than subordinate clauses. A corresponding clause type effect has been identified in the literature for the rise of VO (e.g. Pintzuk & Taylor Reference Pintzuk and Taylor2006: 254–5). On the other hand, Stockwell & Minkova (Reference Stockwell and Minkova1991) articulate the claim that subordinate clauses lead the way in the transition from OV to VO. While my model does include effects that support the general innovativeness of main clauses, it also suggests that the relationship between word order and clause type is much more complicated. Pronoun objects in poetic texts do not provide evidence for the advanced status of main clauses. Nominal objects show a greater dependence on weight in subordinate than main clauses because, for unknown reasons, 2-word objects are more conservative than others in that environment. In general, it remains somewhat vague how the discovery of any distributional difference between clause types in historical corpora would impact on Lightfoot's proposal. There is thus room for a more detailed theory of clause type interactions of the type presented here. Perhaps it is possible to explain the complex influence of clause type on word order variation at least partially in terms of information density (Wallenberg et al. Reference Wallenberg, Bailes, Cuskley and Ingason2021).

The object type effects largely replicate previous findings. The more advanced status of indefinite as compared to definite objects most likely reduces to information-structural constraints already present in Old English. Indefinite objects tend to be new, and newness is a strong predictor for VO order (e.g. Taylor & Pintzuk Reference Taylor and Pintzuk2012). Inversely, definite objects are often old and givenness predicts OV order (e.g. Struik & Kemenade Reference Struik and van Kemenade2020). The distinction appears to be less rigorous in my Middle English data than in previous studies (e.g. the repeated formulation of OV as ‘reserved’ for given objects in Struik & Kemenade Reference Struik and van Kemenade2020) perhaps because poetic requirements like rhyme can easily override general processing constraints. This is illustrated in (6), where an indefinite object, ‘a present’, new to the addressee, nevertheless appears with an OV configuration, quite possibly to enable a rhyme on the main verb.

The finding that quantified, and particularly negatively quantified, objects have a significantly higher probability of occurring with OV than other objects in Middle English ties in seamlessly with identical constraints in the previous Old English (Pintzuk & Taylor Reference Pintzuk and Taylor2004, Reference Pintzuk and Taylor2006) and subsequent Early Modern English periods (Moerenhout & van der Wurff Reference Moerenhout and van der Wurff2000; Ingham Reference Ingham2002). The constraint eventually leads to a narrowing of OV predominantly to these contexts in late Middle and Early Modern English. One interesting question that does not seem to have received great attention so far is to what extent this state of affairs was sustained by influence from French, as illustrated in (7).

1. (7) (a) French

   car nul enuieulx ne peut [O riens] [V souffrir]

   (b) Middle English translation

   for an envious man may [O no thing] [V suffer]

   (King Ponthus and the Fair Sidone, Mather Reference Mather1897: 64)

The issue might be resolved by separating negative indefinite pronouns (nothing) from negatively quantified nominals (no + noun).

It has been claimed that subjectless clauses are another context permitting OV alignments in late prose (van der Wurff 1997). However, ‘Subject’ (overt versus null) did not emerge as a significant variable in any of my models. Perhaps the special context can be explained as a late exaptation of remnant OV order for a new use (e.g. Traugott Reference Traugott2004). More generally, the finding raises the important methodological question of how to distinguish between unquantifiable and non-existent effects.

I will spend the remainder of my discussion on what I believe to be a fundamental and recurring problem arising when historical linguists attempt to map estimates of effects from quantitative models onto phrase-structural grammar models. I will illustrate the problem with reference to the results from the present study.

One can make a rough distinction between two types of studies of historical syntax, which I will call ‘statistical’ and ‘generative’. Statistical treatments attempt to predict the rate of a variant of a linguistic variable from quantifiable and additive factors. Generative treatments, on the other hand, attempt to build a grammar algorithm that can parse a string as a hierarchical phrase structure tree or reject it as ungrammatical. This article is an example of a statistical study, modelling variation in the form of a regression analysis, and not a generative study, as I have not contributed to the development of an explicit, phrase-structural model of Middle English syntax.

Generative analyses are important because they model syntactic structures in a computationally rigid way (e.g. through merge in Minimalism, context-free rewrite rules etc.). For example, the innovation in VO order can be modelled as the gradual emergence of speakers’ knowledge to put together an initial verb with a subsequent object to form a VP, as shown in (1b). In particular, generative models can reveal structural ambiguities. This is a crucial contribution to historical syntax, where it is often the case that linguistic examples may represent an innovative structure of interest, but may also instantiate an alternative parse. In the context of reanalysis, such cases are often referred to as bridge contexts. For example, the existence of ME patterns such as V-X-O (where X can be any constituent) shows that a realistic grammar algorithm for that language stage must be able to handle post-posing structures (extraposition, rightward raising, heavy NP shift etc.). Consequently, the string VO should also be parsable with such an option resulting in ambiguity between two hierarchies, say, underlying VO, […[VO]], and post-posed VO, [[…V]O].Footnote ¹² The methodological question I wish to draw attention to is this: how can one reconcile the prediction of the frequency of word order patterns from statistical models with the insight that some tokens may instantiate extraneous rather than the relevant phrase structure parses from generative models? In the present case, a logistic regression modelFootnote ¹³ predicts VO order from a number of independent variables, but it is wholly uninformative about the frequency of distinct phrase structural parses of these VO patterns, such as an integral VP versus post-position.

While I do not currently have a good solution for this problem, I can see a number of possible paths forward. A first attempt may be to break down the statistical model for specific constructions that are assumed to diagnose the phrase structure of interest unambiguously. For instance, pronominal objects cannot readily post-pose and are hence less problematic with respect to the ambiguity discussed. Generative models can make phrase structure rules sensitive to a categorical difference between pronominal and full nominal phrases. In this way, the grammatical difference can map directly onto separate statistical models. Indeed, I have presented separate models for pronominal and nominal objects above. This method, however, has a number of shortcomings. It is inadequate for distinctions that are probabilistic in nature. For example, the significant effect of object type cannot readily be addressed in this way. In fact, some linguists may not assume a categorical difference even for pronominal and full noun phrases, but rather regard the difference as falling at the extreme end of a spectrum regulating the probability of post-position, since demonstrably post-posed pronoun objects, although extremely rare, are not entirely absent in Middle English. Furthermore, there will often be so many structural ambiguities that no one sensible subdivision is obvious. Not infrequently may subdivisions require the isolation of some data that disambiguates a parse of interest and at the same time the inclusion of the same data in the combined dataset as it is ambiguous with respect to some other kind of syntactic ambiguity. For example, pronominal objects do not normally post-pose, but they frequently pre-pose, creating ambiguous parses for OV orders. At best, the separate model for pronominal objects therefore estimates a lower bound for the true rate of underlying VO order with pronouns.

Another avenue could be inspired by the idea to correct the total counts of an ambiguous syntactic structure by estimating the rates of their distinct parsing alternatives. For example, one could compare V-X-O structures, where O must be post-posed, to V-O-X structures, where O is unlikely to have post-posed, to arrive at an estimate of the rate of post-position. This estimate can be used to calculate the proportion of post-posed objects in ambiguous VO patterns. For an excellent example of this methodology in connection with information structure, see Taylor & Pintzuk (Reference Taylor and Pintzuk2011). However, this approach has problems as well. One cannot be sure that the rate of a particular parse estimated independently from one diagnostic will carry over to another environment. I agree that it is fruitful to work under the assumption ‘that postposing applies at a constant rate’ (Pintzuk & Taylor Reference Pintzuk and Taylor2006: 266) in all environments, but this is not a necessity. In the example above, the rate of post-position in contexts with an additional element X may be different from the rate of post-position in contexts without such an element. I also worry that the method may stretch the relatively small early English corpora beyond their limits so that fluctuations by random factors, like individual texts, may influence the results. I therefore believe that it may be more advantageous to attempt a principled mapping of regression coefficients to a probability for the application of a syntactic process.

Why do I contend that this is an important problem? First, generative analyses can make unfalsifiable claims about the prevalence and time of birth or death of underlying orders because they can freely interpret ambiguous tokens that could but do not have to be parsed according to the underlying order of interest. Exaggerating to illustrate the point, I could claim that VO order arises extremely late, say in 1400, and extremely fast, say within 10 years, by analysing all prior VO strings as results of post-position processes. Intuitively, the best way to inoculate against such absurdities is to tie historical generative models to quantitative corpus data. Yet statistical models do not actually resolve the issue in principle at the moment. Second, an enhanced synthesis of statistical and generative models can help to avoid ideological fragmentation in the field. Usage-based linguists may interpret the output from statistical models as directly representative of speakers’ knowledge thus paying little attention to the reality of structural ambiguity. Generativists may struggle to include probabilistic effects in their discrete phrase structure rules. If successful, a synthesis could build strong bridges between historical syntacticians of different persuasions and between their ideas.