Volume 14 Issue 1 (2016) DOI:10.1349/PS1.1537-0852.A.468 Note: Linguistic Discovery uses Unicode characters to represent phonetic symbols. Please see Optimizing Display for requirements to accurately reproduce this page. Exploring grammatical complexity crosslinguistically: The case of gender Francesca Di Garbo University of Helsinki This paper proposes a set of principles and methodologies for the crosslinguistic investigation of grammatical complexity and applies them to the in-depth study of one grammatical domain, gender.
The complexity of gender
is modeled on the basis of crosslinguistically documented properties of gender systems and by taking into consideration interactions between gender
and two other grammatical domains: nominal number and evaluative morphology. The study proposes a complexity metric for gender that consists
of six features: “Gender values”, “Assignment rules”, “Number of indexation (agreement) domains”, “Cumulative exponence of gender and number”, “Manipulation of gender assignment triggered by number/countability”, and “Manipulation of gender assignment triggered by size”. The metric
is tested on a sample of 84 African languages, organized in subsamples of genealogically related languages. The results of the investigation show that: (1) the gender
systems of the sampled languages
lean towards high complexity scores; (2) languages with purely semantic
gender assignment tend to lack pervasive
gender indexation; (3) languages with a high number of gender distinctions tend to exhibit pervasive
gender indexation; (4) some of the uses of manipulable gender assignment are only attested in languages with a high number of gender distinctions and/or pervasive
indexation. With respect to the distribution of the gender
complexity scores, the results show that genealogically related languages tend to have the same or similar
gender complexity scores. Languages
that display exceedingly low or high gender complexity scores when compared
with closely related languages exhibit distinctive sociolinguistic profiles (contact,
bi- or multilingualism). The implications of these findings
for the typology
of gender systems
and the crosslinguistic study of grammatical complexity and its distribution are discussed. 1. Introduction Investigating the complexity of individual grammatical domains from a crosslinguistic perspective is still a novel research area within language
typology. This paper focuses on the empirical
study of grammatical complexity and proposes a set of principles and methodologies that can be operationalized to explore
linguistic complexity crosslinguistically.[1] The
paper takes inspiration from the suggestions made by Miestamo (2006b, 2008) on the typological study of grammatical complexity. According to Miestamo, complexity metrics suitable
for typological purposes should
not aim to assess the grammatical complexity of languages in their entirety (global complexity), but rather focus on specific
domains of grammar (e.g. functional domains)
as encoded across
languages, and attempt
to characterize “the cross-linguistic variety in the complexity of each functional domain and the interactions between domains”
(2006b) (local complexity). The grammatical domain that I investigate in this paper
is grammatical gender. Gender is
a type of nominal classification device (in the sense of Aikhenvald 2003) that is commonly associated with high degrees of complexity, inasmuch as it presupposes inflectional morphology (agreement) and rather
opaque grammaticalization
paths (Corbett 1991; Dahl 2004; Nichols 1992). In this study, I attempt to model the complexity of gender by identifying a set of dimensions that
characterize gender systems
crosslinguistically and by taking into consideration interactions and possible asymmetries between
gender and two other nominal grammatical domains, number and evaluative morphology. The paper proposes a complexity metric for gender. This metric is then tested
on a sample of 84 African languages. The aim of the paper is to investigate whether crosslinguistic variation in the types of gender systems
attested in the sample languages is tied to certain levels of complexity, and why this might be the case. In addition, by exploring
gender complexity within and across genealogical groupings, the study aims to investigate to which extent the complexity of gender – a morphosyntactic feature that is usually conceived of as very stable
in the history of language
families – is conservative across related languages and under which
conditions it is subject to decrease or increase. The paper is structured as follows.
In section 2, I define
the notion of grammatical complexity that I work with. In section
3, I introduce gender as a grammatical domain and consider possible
dimensions for the assessment of gender complexity. The methodology followed in the study is illustrated in section 4: section 4.1 provides an outline of the sampling
procedure; section 4.2 presents the complexity metric and section
4.3 illustrates the method used to compute
complexity scores
for the gender
systems of the sampled languages. The results are presented in section 5 and discussed in section 6, before I provide
some concluding remarks
in section 7. 2. Defining grammatical complexity The idea that all languages are equally complex is known in the literature as the equi-complexity hypothesis and is based
on the assumption that, even though
individual languages may exhibit
different levels of complexity in different
domains of their grammars,
complexity in one domain
is compensated by simplicity in another
domain (complexity trade-offs). The equi-complexity hypothesis has long been maintained as a truism within
linguistic research (for an overview, see McWhorter 2001; Kusters 2003). During the past fifteen
years, however, starting from the comparative study of grammatical complexity in creole and non-creole languages by McWhorter (2001), a whole body of research (see, among others, Dahl 2004; Kusters 2003; Miestamo 2006b; Miestamo et al. 2008; Sinnema¨ki 2011) has suggested that the equi-complexity hypothesis is difficult to
test empirically and that, when tested (e.g., by McWhorter 2001), it is actually problematic to maintain. In a nutshell, this research has shown that “there
is no principled reason
why all languages should be equal in their overall complexity or why complexity in one grammatical area should be compensated by simplicity in
another” (Miestamo 2006b). Once we acknowledge that human languages
may differ in complexity,[2] and that these differences are worth exploring
for a multifaceted array of purposes
(typological, sociolinguistic, historical, etc.), three major challenges follow: (1) how to define complexity; (2) how big a scope a complexity metric should have for it to be meaningful, and (3) which principles might help to assess complexity differences in one or several domains of grammar. The three issues are discussed
in section 2.1, section 2.2, and section 2.3, respectively. 2.1 Absolute and relative complexity There exist two main approaches to the study of linguistic complexity, the relative and the absolute approach (Miestamo 2008). The relative
approach (also known as user-oriented approach) focuses on the costs and difficulties
in language learning and processing. The absolute approach
(also known as theory-oriented approach) rather views complexity as an objective property
of languages. Within the absolute approach, complexity can be assessed
by measuring the number of distinctions within a system/grammatical domain,
and the length of its description. Both approaches have been used, and argued for, in typologically oriented
literature on grammatical complexity. Kusters (2003), for instance, defines
complexity in terms
of difficulty. In his work on the typology
of verbal inflection, Kusters examines
four genealogically unrelated
sets[3] of closely related languages and investigates how, within each set, languages differ in the complexity of verbal inflection and what type of sociolinguistic and sociohistorical factors may account for these differences. His definition of complexity is based on the difficulties – as documented in the psycholinguistic literature on second language acquisition – that
adults incur when learning a new language. According to this definition, languages that are more “adapted” to the presence of L2 learners (exoteric languages, following the terminology proposed by Lupyan & Dale 2010) are less complex than languages that, throughout their history, have not been exposed, or not to the same extent, to the presence of adult learners (esoteric languages, based on Lupyan & Dale 2010). This definition of complexity/difficulty fits well the scope of Kusters’ (2003) study, which is to investigate the effects
of multilingualism, asymmetrical bilingualism
and adult language
contact on language
structures. However,
as Miestamo (2006b) rightly points
out, L2 learners represent only one type of language users. In addition, adult, post-critical threshold language
contact is only one type of contact scenario
in the history of a speech community.[4] It follows that a definition
of complexity/difficulty that is targeted to one category of language users only might
not be inclusive enough if our aim is
to build a more general model of linguistic complexity. Finally, given our still limited
knowledge of the cognitive processes behind language learning
and usage, we do not have enough evidence to model the whole range of difficulties and costs that both L1 and L2 speakers and listeners experience when using language. Thus, based on our current
state of knowledge, the absolute approach allows for a more
general, objective, definition of the notion of complexity. This is in turn essential
for the sake of crosslinguistic comparison. In addition, the absolute approach to grammatical complexity is the one that is more easily connectable with how complexity is
approached by other disciplines (e.g., philosophy, information theory) and thus “opens possibilities for interdisciplinary research” (Miestamo 2008: 27). Advocates of the absolute approach to the typological study of grammatical complexity are, among others, McWhorter (2001); Dahl (2004); Miestamo (2006b, 2008); Nichols (2009); Sinnema¨ki (2011). The absolute approach is followed in this paper. Accordingly, I use the term complexity to refer to absolute complexity and the term difficulty to refer to relative complexity. 2.2 Global vs. local complexity One issue that has been at the center of the recent debate on grammatical complexity is how big a scope a complexity metric should have for it to be meaningful. McWhorter (2001) elaborates a complexity metric
that aims to measure overall differences in the grammatical complexity of creole and non-creole languages. The metric captures
phonological, morphological, syntactic
and semantic patterns
that involve various types of redundancy
(in terms of number of overt distinctions and amount of rules) and thus qualify a language as more complex than another.
Two languages
are investigated in the first part of the
study, the highly
inflectional language Tsez (Nakh-Daghestanian) and the creole
language Saramaccan. The metric individuates
clear-cut complexity differences
between the two languages: Sarammaccan systematically qualifies as simpler
than Tsez with respect to all the parameters under investigation. In the second
part of the study, the same complexity metric is used to compare
Saramaccan with an non-creole analytic
language, Lahu (Sino-Tibetan), based on the hypothesis that “the complexity difference between creoles and analytic
languages would be less than that between them and inflected languages” (McWhorter 2001: 143). Nevertheless, the comparison reveals complexity differences between
Saramaccan and Lahu that are similar to those found for Tsez and Saramaccan. These results would seem to confirm McWhorter’s hypothesis whereby the grammar of creole languages is systematically simpler than that of non-creole languages. The question
however remains whether a metric of this type could be effectively used to capture complexity differences (1) between a higher number of languages than those considered in McWhorter’s study, and (2) based on a sampling
procedure that is independent of the creole/non-creole dichotomy. Developing a metric that would satisfy these conditions and would allow us to compute
the total complexity of a language
in typologically meaningful ways is ultimately a massive,
daunting task (see also discussion in Miestamo 2006b, 2008; Nichols 2009). In addition, even if, as suggested by Nichols (2009: 111), one would be able “to draw a representative sample of complexity in enough different
grammatical domains, relatively easy to survey, to
give a reliable indication of whether overall complexity does or does not vary”, it would be still very hard (and probably
even impossible) to establish the mutual comparability between the criteria used in the metric. In other words, it would be extremely difficult to decide whether,
for instance, the number of tense distinctions, phonemes, or gender distinctions that are grammaticalized in a given language
contribute in the same way to the total complexity of that language.
Miestamo (2006b, 2008) refers to this as the problem of comparability and suggests that in view of this difficulty, the crosslinguistic study of grammatical complexity should
be based on individual areas of grammar, such as functional domains, rather than on grammars
in their entirety, and thus have a local rather than global scope. In this paper, I follow this suggestion and investigate the complexity of one grammatical domain, gender. In addition, based on Dahl (2011), I argue that in order to be maximally local, complexity metrics should be based on ceteribus paribus comparisons, that is on statements of the type: “Everything else being equal,
X is more complex
than Y.” 2.3 Complexity
principles In this study, I suggest
that, within an absolute and local approach
to grammatical complexity (see section 2.1 and 2.2), three principles can be used as general guidelines to define the variables of a complexity metric:
the Principle of Fewer Distinctions, the Principle
of One-Meaning–One-Form and
the Principle of Independence. The first two principles are well established in the literature on grammatical complexity (for an overview, see Miestamo 2008). The third principle, the Principle of Independence, was introduced
by Di Garbo (2014) to account for interactions between functional domains
and complexity. In the following, I outline my definitions of the three principles: •The Principle of Fewer Distinctions (proposed by Miestamo 2006a, 2008 and also known as Principle of Economy, see e.g., Kusters 2003):
Everything else being equal, a grammatical domain with n distinctions is less complex
than one with n+1 distinctions. •The Principle
of One-Meaning–One-Form (well established in the literature
on theoretical morphology and linguistic complexity, also known as the Principle
of Transparency, see, for instance,
Kusters 2003): (a) Everything else being equal, a grammatical meaning
with n
forms is less complex
than one with n+1 forms; (b) Everything else being equal, a grammatical form with n meanings is less complex than one with n+1 meanings. •The Principle of Independence (introduced by Di Garbo 2014):[5] Everything else being equal, a grammatical domain that is independent
of semantic and functional properties of other domains is less complex than a grammatical domain that is dependent on n or n+1 semantic and functional properties
of other grammatical domains. The Principle of Fewer Distinctions is concerned with the type and number of grammatical meanings
that a language expresses
within a given domain of grammar. For instance,
other things being equal,
a language with more than five genders (e.g., Swahili) is more complex in this respect than a language with three genders only (e.g.,
German). The Principle
of One-Meaning–One-Form has to do with the type of encoding of a grammatical meaning within a given domain of grammar. The Principle of One-Meaning–One-Form can be operationalized in two ways, depending on whether
we consider the mapping between
form and meaning or, vice versa, the mapping between meaning and form. In addition, as suggested by Miestamo (2008: 33), the relationship between form and meaning can be investigated both at the paradigmatic and syntagmatic level. For instance,
with respect to the encoding of standard negation, Italian, whose standard negator is non, is, other things being equal, less complex than French,
which typically uses a discontinuous marker, ne...pas, to signal standard negation. Or, similarly,
other things being equal, Turkish is simpler than German with respect to the type of exponence of case and number. In Turkish,
the two grammatical meanings
are encoded separately
(one form for each meaning), whereas
in German, number and case are encoded cumulatively (one marker for several meanings).[6] Both these violations of the Principle of One-Meaning–One-Form operate
on the syntagmatic level. On the other hand, phenomena
such as allomorphy and syncretism represent
a violation of the Principle
of One-Meaning–One-Form at the paradigmatic level. Finally, the Principle of Independence models interactions between
domains and their effect on complexity. For instance, a language in which gender
assignment is dependent on evaluative meanings – if, e.g., masculine
nouns can be shifted to the feminine
gender when a diminutive meaning is encoded
(as in the Berber language Kabyle)
– is more complex
in this respect than a language
in which gender assignment cannot be manipulated for such purposes
(as in the Romance language
Italian). In the remainder of this paper, the Principle of Fewer Distinctions, the Principle of One-Meaning–One-Form and the Principle of Independence will be operationalized in designing a complexity metric for grammatical gender. 3. Grammatical gender and dimensions of
gender complexity 3.1 Gender as a grammatical domain In this paper, I follow the most widely accepted definition of gender within the typological literature (Corbett 1991; Hockett 1958). Thus I define gender
as a type of nominal classification strategy that must be reflected beyond nouns, via agreement
patterns (Di Garbo 2014: 3). Under this definition I include both systems of the Bantu type (large number of genders)
and systems of the Romance
type (small number of genders). Following Croft (2001, 2003, 2013), however, I refer to agreement patterns
as indexation patterns. Accordingly, I define the entities
whose inflectional morphology
signals gender (e.g., pronouns, adjectives, verbs) as gender indexes (or gender
indexing targets) and the entities that trigger a given gender indexation pattern (i.e., nouns, pronouns, noun phrase referents) as indexation triggers. In Corbett’s (1991) terminology, indexes and indexation triggers
are referred to as agreement
targets and controllers, respectively.[7] In the remainder of this section, I provide
a short overview of the criteria
used for the synchronic classification of gender systems,
the debate over the origins of gender, and the function(s) of gender
in discourse. Synchronically, the gender systems of individual languages are usually
classified based on: (1) the number of gender distinctions (Corbett 1991, 2013a); (2) whether gender distinctions are sex-based
or non-sex-based based (Corbett 1991, 2013b); (3) the criteria according
to which nouns are assigned
to a given gender (Corbett 1991, 2013c). Diachronically, gender has been observed to be one of the most stable features of grammar. Gender systems are stable with respect to two of the three criteria
for stability proposed
by Nichols (1992): diachronic persistence and areal contingency. Gender is one of the most conservative features in the history of language families (stability as diachronic persistence). For instance, Armenian
is the only independent branch
of the Indo-European language family that has completely lost
grammatical gender. In addition,
gender systems exhibit a hotbed–outlier type of distribution (stability as areal contingency): some areas of the world, such as Africa or Australia,
are densely populated
by languages with gender (gender hotbeds), whereas in other
areas of the world (e.g., North America), the feature
is absent or attested only in isolated
cases (gender outliers). The debate over the origins
of gender is very controversial and, in many respects, still unresolved. On the one hand, it has been shown that gender
systems may originate
from classifier systems
and/or from demonstratives (Greenberg 1978; Corbett 1991). On the other hand, among the issues that are still open for debate is, for instance,
the question of whether indexation or classification comes first in the diachrony of gender within a given language or language
family (Nichols 1992). The main difficulty behind the reconstruction of the diachrony of gender in many language families is that, in view of their
overall stability, gender systems tend to presuppose long grammaticalization paths
and their origin often precedes
those stages that can be reconstructed via the historical-comparative method. Finally, from a functional point
of view gender has been defined
as a grammatical device for the management of reference in discourse, its functions being often related to reference tracking
(Heath 1975; Foley & Van Valin 1984) and/or discourse redundancy (Dahl 2004). The debate over the discourse functions
of gender is huge and cannot be extensively surveyed here (for an overview,
see Kilarski 2013: chapter 6, as well as Contini-Morava & Kilarski 2013). For the sake of this paper, suffice it to say that scholars
usually disagree on whether the complex redundancies that gender indexation introduces in discourse
facilitate communication (Dahl 2004) or exist beyond communicative necessity
(McWhorter 2001). Evidence from second language
acquisition is often brought in support of the latter argument:
contact varieties
that emerge as a result of intensive
post-threshold language contact and nonnative acquisition tend to systematically lack gender; similarly, adult learners usually struggle with grammatical gender when acquiring
a new language. 3.2
The dimensions of gender complexity Together with verbal inflection (Kusters 2003) and core argument
marking (Sinnema¨ki 2011), gender figures as one of the few areas of grammar
that have, so far, received
some attention in the literature on linguistic complexity. Perhaps this is because grammatical gender is one of the domains of grammar that most promptly leads itself to be associated with complexity, being both theoretically and empirically relevant for the study of such notions as inflectional morphology (Nichols 1992), maturity (Dahl 2004) and redundancy in information management (McWhorter 2001). Grammatical gender, in the form of gender indexation and overt gender distinctions on nouns, is one of the features
of the complexity metric
proposed by Nichols (2009). In this study, properties of gender systems are surveyed
together with properties of other nominal classification devices (numeral and possessive classifiers) under the label classification. Within the metric
proposed by Nichols,
presence of gender indexation and overt marking of gender on nouns feature
higher degrees
of complexity.[8] A more detailed qualitative study of the dimensions of gender complexity – viewed independently of other nominal
classification devices
– is Audring (2014). Audring argues that the complexity of gender systems
is tied to and can be investigated by taking
into considerations three main dimensions: complexity of values; complexity
of assignment rules; and complexity of formal marking. Dimension 1, complexity of values, is concerned with the number of genders
in a language:
the higher the number of genders, the more complex the gender system. Dimension 2, complexity of assignment rules,
is concerned with the type and scope of gender assignment rules. With respect to type of assignment rules, the literature on the typology
of gender systems
(Corbett 1991, 2013c) has shown that there exists two principles according
to which nouns are assigned to a gender in a given language: semantic and
formal. Under semantic assignment rules, gender assignment is predicted on the basis of the meaning of nouns. Under formal assignment
rules, gender assignment is predicted based on morphological rules (e.g., inflectional classes, derivational morphology) and/or phonological rules. In principle, the least complex gender system is one in which
only one type of assignment rule is attested, semantic or formal. In reality, typological studies of gender (Corbett
1991, 2013c) have shown that while solely semantic gender systems are relatively common
among the world’s languages
(e.g., among Dravidian
languages), gender systems
purely based on formal assignment rules are almost
never encountered. Even in those systems that are heavily
skewed towards formal
mechanisms of gender assignment, there is always at least a minimal portion of the nominal lexicon
(often nouns denoting humans and/or animate entities)
for which gender is
assigned based on clear-cut
semantic criteria.[9] As for the scope of assignment rules, this has to do with the degree of generality of a rule, that is the gender assignment of how many nouns a given rule is able to predict. The higher the number of nouns assigned to a certain
gender by a given assignment rule, the larger the scope of the assignment rule. In general,
a system with large assignment rules
requires a lower number
of rules, leading
to lower complexity. These rules usually
rests upon some basic
semantic notions such as sex or animacy
(Audring 2014: 11). Dimension 3, complexity of formal marking, is concerned with the pervasiveness of gender marking in discourse, that is, via indexation. The most straightforward implementation of this dimension of the complexity of gender is to count how many gender
indexes there are in a language based on how many
word classes inflect
for gender (e.g.,
pronouns, adjectives, verbs), and independently of how these inflections are realized in discourse. The higher the number of gender indexes, the greater the complexity of a gender system.
However, it is
also possible to explore this dimension of gender complexity by looking at discourse frequencies, that is by measuring how
often gender inflections appear in a given
chunk of discourse (the higher the frequency
of gender marking in discourse, the more complex the system). This aspect
of the complexity of gender (which will not be explored
further in this paper) can also be operationalized in the investigation of the functionality of gender indexation in language learning
and processing. In this sense,
a particularly promising hypothesis that is put forward in Audring’s (2014) work is that, pervasive gender indexation facilitates the learning and processing of gender values and assignment rules, given that users are exposed
to multiple occurrences of gender marking in a given chunk
of discourse. The gender system of English would rank low with respect to all three dimensions of complexity: it has only three genders,
a few semantic assignment
rules, and gender indexation is restricted to the pronominal domain. To sum up, Audring (2014) suggests that the absolute
complexity of gender systems can be explored
on the basis of three macro-dimensions: number of values,
assignment and indexation. This suggestion is followed
in the present paper. In section 4.2, I propose one way of implementing the three dimensions
into a complexity metric. 4. Methodology 4.1 Sampling procedure This study is based on a sample of 84 gendered
languages selected from the African macro-area and organized
in subsets of genealogically related
languages (the sample
languages are listed in alphabetical order
in appendix A).[10] The macro-area sampled in the study, Africa, is one of the world’s gender hotbeds (Nichols 1992, 2003): all major genealogical groupings
within the area display gender at least at some level of their internal taxonomies. The language classification followed in the paper is the one proposed by Glottolog (Nordhoff et al. 2013) as of September, 2015. The sample designed
for this study differs
from classical sampling
procedures in linguistic typology. Traditionally, these procedures aim to maximize
the representation of
linguistic diversity by contributing one datapoint (i.e., one language) per genealogical unit.[11] In recent years, statistically implemented sampling
methodologies that attempt
to investigate linguistic patterns
as distributed within language families have been proposed, for instance, by Maslova (2000) and Bickel (2013). The main assumption behind
these methodologies is that typological distributions concerning linguistic variables reflect
different historical scenarios that may
favor the presence/development/maintenance or, rather, the absence/decline/loss of the variables
in question. Accordingly, these
studies argue that it is possible
to explore “statistical biases
in diachronic developments on the basis of synchronic samples” (Bickel 2013: 415). The design of the present sample is built on similar assumptions. However the study does not focus on the elaboration of stochastic models of language change
based on the observation of synchronic distributions. The aim of the study is, in fact, mostly descriptive. What I am looking for is the degree of grammatical complexity that is associated with gender crosslinguistically and the extent to which this complexity is genealogically and areally uniform. The sample consists of seventeen different genealogical units (or lineages following the terminology by Nichols 1992), among which two isolates (Hadza and Sandawe). Some of these units represent different subgroups of the same superordinate taxonomic level (stock)[12]. In general, language
selection has been guided by the following
rule of thumb: the higher the diversity (in terms of number of languages/subgroups) of a superordinate genealogical unit,
the higher the number of languages/subgroups selected
for that unit. Consequently, the biggest and more diverse language families are represented by a number of subsamples
that tends to reflect this diversity. For instance,
all major subdivisions of the Afro-Asiatic stock (except
Egyptian) are represented in the sample.
The subsamples created for each stock should be understood as convenience samples since (1) the number of languages
per genealogical units is not established mathematically and (2) for the biggest stocks, not all subdivisions are included. The latter especially
applies to the largest
stock within the African macro-area, Atlantic-Congo. Some relevant genealogical units, such as Kru and, from the Volta-Congo sub-branch, Gur and Ubangi are, for
instance, not included in the sample mainly due to lack of accessible resources. This impacts data analysis
in that the data-set created for this study
cannot be used for statistical
analysis of the inferential type, that
is to make predictions about preferred typological patterns in the languages of Africa and beyond. Thus, as mentioned
above, the statistical analysis
that will be applied to the data presented in the study
is purely descriptive. Table
1 illustrates the number of genealogical units/languages per stock.
Table 1. Genealogical units in the sample 4.2 The features of the complexity metric The complexity metric that I designed for the purpose
of this study consists of six features. These can be further grouped into three main domains,
which are based on the three dimensions of gender complexity proposed
by Audring (2014) and discussed in section 3.2: complexity of values, complexity of rules and complexity of formal
marking. The features
of the complexity metric
are presented in table 2.
Table 2. Features
of the complexity metric and their description Features GV, AR and IND can be seen as direct implementations of Audring’s (2014) three dimensions of gender complexity. Complexity with respect to GV counts as a violation
of the Principle of Fewer Distinctions (the higher the number of gender distinctions, the more complex the system). Less straightforward is, on the other hand, the interpretation of AR and IND with respect to the three complexity principles outlined in 2.3. Here, I propose
to view complexity with respect to AR as a violation
of the Principle of Independence, and complexity with respect to IND as a violation
of the Principle of One-Meaning–One-Form (both on the syntagmatic and paradigmatic level) and the Principle
of Independence. On the one hand, systems of gender assignment that are dependent
only on semantics or only on form are less complex than systems of gender assignment that are dependent
both on semantics and form (violation of Principle of Independence). On the other hand, in a language
in which many word classes inflect for gender, and gender inflections are attested in several indexation domains (e.g., articles,
other adnominal modifiers, predicative expressions, pronouns): (a) information about the gender of a noun is likely to be repeated
redundantly in discourse (syntagmatic violation of the Principle of One-Meaning–One-Form); (b) the same word class can take several inflections depending
on the gender
of the noun that is indexed
in a given discourse
domain (paradigmatic violation of
the Principle of One-Meaning–One-Form
and Principle of Independence). Features M1, M2 and CUM are based on an aspect of the typology of gender that falls outside the scope of Audring’s
work: how grammatical gender interacts with other nominal
domains. Two domains are specifically targeted by my metric: number and evaluative morphology (i.e., the morphological encoding of diminutives and augmentatives).[15] M1 and M2 are concerned with interactions at the level of gender assignment whereas CUM has to do with interactions pertaining to the morphosyntactic encoding of gender
distinctions on the indexing
targets. I suggest that M1 and M2 can be interpreted as a violation of the Principle of Independence, and CUM as a violation
of the Principle of One-Meaning–One-Form. Let us discuss
these two types of interaction more in detail. Di Garbo (2014) shows that an important criterion for the classification of gender systems in the African
macro-area is to distinguish between
rigid and manipulable
gender assignment (for as similar suggestion, see also the study by Heine 1982). In languages with manipulable gender assignment, the gender of a noun can be changed depending
on the construal of the noun phrase referent, that is based on pragmatic/discourse constraints. In these languages, there usually are default assignment rules, i.e., rules by which nouns have lexically specified
gender values, and add-on assignment rules that allow speakers to modify the default
meaning of the noun by changing
its gender, thus changing
the construal of the noun phrase referent. In Di Garbo’s sample,
manipulable gender assignment is attested in connection with two main uses: (1) to encode variation in the countability properties
of nouns (e.g., from uncountable to countable and vice versa), (2) to encode variation
in size (diminutive vs. augmentative). In my metric,
I refer to the first use of manipulable gender assignment as M1[16] and to the second as M2. M1 is illustrated in example (1) and M2 in example (2). The examples
are taken from two Berber languages, Nefusi and Tachawit.[17]
In example (1) (taken from Nefusi),
when the inherently masculine uncountable noun ettefˆah̩ ‘apples’ is shifted
to the feminine gender (as in (1b)), it becomes countable and can be thus regularly
pluralized (as in (1c)) (in Berber, feminine
gender marking on nouns is circumfixal both in the singular
and in the plural). This is an instance of M1. In Tachawit
(example (2)), inherently masculine nouns can be shifted to the feminine gender when a diminutive interpretation is intended for the noun phrase referent
(as in (2a) and (2b)). Similarly, an inherently feminine noun can be assigned
to the masculine gender when an augmentative interpretation is intended for the noun phrase referent (as in (2c) and (2d)). This is an instance
of M2.[18] In general M1 and M2 are well attested in the languages of Africa, both in
languages with large, non-sex-based gender
systems and in languages with smaller sex-based
systems. Within my sample, M2 is however more frequent
and widely distributed than M1 (for an overview, see Di Garbo 2014: chapters 5 and 6). The possibility of manipulating gender assignment can be seen as piling on top of the default
gender assignment
rules that are used in a language.
In languages with manipulable gender assignment, gender
markers have default and add-on meanings.
These add-on meanings are dependent on semantic and pragmatic associations between
gender and other grammatical domains, notably countability and size/value. Thus, based on the Principle
of Independence introduced above, their presence represents an increase in the absolute
complexity of gender.
Gender assignment is not only given in the lexicon for each and every noun, but it is also subject to change depending on semantic
and pragmatic associations with other functional domains. Feature CUM (cumulative encoding of gender and number on the indexing targets) evaluates the impact that type of exponence
of gender and number has on the complexity of gender. I interpret cumulative encoding of gender and number as a violation
of the Principle of One-Meaning–One-Form (one morpheme expresses several grammatical meanings). One aspect of the morphosyntactic encoding of gender and number which,
at least in the languages
of my sample, appears to be strictly
related to CUM is the tendency
for gender distinctions to be reduced (syncretism) or lost (neutralization)
in the context of non-singular number values. In my sample, syncretism and/or neutralization of gender in the context of nonsingular number
occurs in 66 out of 84 languages; in nearly all these cases the languages in which syncretism is
attested are also languages in which gender and number are encoded cumulatively (see also results
in Di Garbo 2014: chapter 5).[19] In principle,
gender syncretism and neutralization could be viewed as violations of the Principle of Independence inasmuch
as, when they occur, the expression
of gender within an inflectional paradigm depends on the number value of a noun. In addition,
syncretism and neutralization could be also seen as violations of the Principle
of One-Meaning–One-Form, given that two (or more) gender values are conflated into one in the context
of non-singular number
values. However, as Audring (forthcoming) points out, “[s]yncretism is a
multifaceted phenomenon, and whether or not it should be considered a case of simplification or complexification depends on the perspective”. In this paper, I treat syncretism in a somewhat agnostic
way and exclude
it from my complexity metric. More research,
I believe, is needed on the relationship between
syncretism/neutralization, exponence, and paradigm size before
we can assess the effects of syncretism/neutralization on the complexity of gender and related features (e.g., number and case) more
confidently. 4.3 Method for computing Gender Complexity Scores Having defined the features for measuring the absolute complexity of grammatical gender (see table 2), the next step is to establish the values associated with each feature and to convert them into numbers. Towards this aim, I follow Parkvall (2008) who designed a method for computing the grammatical complexity of creoles and non-creole languages on the basis of a set of features taken from the WALS database (Dryer & Haspelmath 2013). Within Parkvall’s method, the values of each feature are assigned a number between 0 and 1. Features with three values are converted into the numerical format 0, 1/2, 1. Similarly, features with five values are converted by Parkvall into the format 0, 1/4, 1/2, 3/4, 1. For all the features taken into account in Parkvall’s paper, 0 stands for minimally complex and 1 for maximally complex. The total complexity score for each language is divided by the number of features included for that language. This is done in order to allow languages for which less information is available on a given feature to get average scores comparable to those of the best documented languages. The same procedure is followed in this paper (naturally, features with four values are converted into the numerical format 0, 1/3, 2/3, 1). The feature values and their numerical interpretation are illustrated in table 3.
Table 3: Gender complexity metric The composition of the metric is such that the least complex possible
gender system is the one that scores zero with respect
to all the features of the metric and exhibit
the following properties: two gender values,
semantic gender assignment, one indexing
target, no cumulation with number, no manipulation of gender assignment triggered
by number/countability and no manipulation of gender assignment triggered by size. On the other hand, the most complex
possible gender system is the one that scores
1 with respect to all the parameters considered in the metric and exhibits
the following properties: five or more genders, semantic and formal assignment, four or more indexing
targets, cumulation with number, and manipulation of gender assignment triggered by both number/countability and size. In addition, the composition of the metric is such that, with the exception of languages
with the highest
score (= 1), languages may display the same index value but arrive to it on different paths. In other
words, identical gender complexity scores (henceforth GCSs) do not stand for same type of gender
system. Before presenting the results of my calculations, it is worth mentioning that, in case of missing features, the index values
resulting from the calculations should be taken with caution.
In fact, even though average scores
(rather than total scores) are used as index values, the index values of languages
with missing features cannot be regarded
as entirely comparable to the index values of languages for which all features are equally
represented. The mutual comparability between
the different
domains of gender
complexity covered by my metric
is discussed in section 6.3. 5. Results Table 4 illustrates the GCSs of the languages of the sample,
which have been calculated based on the method presented
in section 4. The table is divided in two macrocolumns and the GCSs of the individual languages are arranged
from highest to lowest. The leftmost columns of each macro-column provide
the rank: languages with the same average complexity score share the same rank.
Next to the rank come the language
names and their
ISO code; the GCS assigned
to each language
is given in the rightmost columns of the two macro-columns. In appendix C, the GCSs are visualized on the basis of genealogical units. The complexity scores
for each of the feature
values in the metric,
as well the GCSs, are given in appendix
B.
Table
4: GCSs of the languages of the sample Table 4 shows that the highest GCS is 1 and the lowest 0.08. None of the languages of my sample thus gets the lowest possible
score, 0 (see section 4.3). The results given in table 4 are also displayed
in the graph in figure 1. The X-axis of the histogram
displays the range of attested
GCSs, whereas the Y-axis shows the distribution of the number of languages
per GCS score. The box plot below the histogram provides
the distribution of the GCSs per quartiles, with the boldface line in the middle representing the median. The figure shows that half of the languages of my sample have a GCS that ranges roughly from 0.5 to
0.8. In my data sample, high GCSs are substantially more frequent than low GCSs. Figure 1: Distribution of the GCSs The geographical distribution of the GCSs is represented in the map provided
in figure 2. Figure 2: Geographical distribution of the GCSs The results presented in table 4, figure 1 and 2, as well as in appendix C, are discussed
in section 6 based on three main foci:
Before moving on to the discussion, I illustrate the procedure followed to calculate the GCSs of two of the sampled languages.
For the sake of clarity, I discuss one language for which all features are documented, Turkana (Eastern
Nilotic, rank 3 in table 4), and one for which two features are missing, Timne (Mel, rank 1 in table 4). My classification of the gender system
of Turkana is based
on Dimmendaal (1983). Turkana has three gender values: Masculine,
Feminine and Neuter. It thus gets 1/3 with respect to the feature GV. Gender assignment is both semantic and formal, and, as such, the value of AR is 1. According to Dimmendaal, gender indexation appears in three domains: articles
(definite articles), adnominal
modifiers, and pronouns
(not the Personal
Pronouns). Thus the language gets 2/3 with respect to the feature
IND. In Turkana, gender distinctions are encoded cumulatively with number (CUM = 1). Finally,
in Turkana gender shifts can be used to encode variation
both in the countability properties
of nouns (M1 = 1) and in the size of the
noun phrase referent (M2 =
1). In Turkana, when an uncountable masculine or feminine
noun is shifted to the Neuter Gender,[22] the resulting
meaning is singulative. On the other hand, when countable masculine
or feminine nouns are shifted to the Neuter Gender, the resulting
meaning is diminutive. To summarize, for Turkana,
the values assigned to each feature
of the metric are: GV = 1/3; AR = 1; IND = 2/3; CUM = 1; M1 = 1; M2 = 1 Applying the formula illustrated in section 4 [(⅓+1+⅓+⅔+1+1+1)÷6]
the GCS of 0.83 is obtained. I classify the gender
system of Timne based
on the description provided
by Wilson (1961). Timne has more than five genders and thus gets 1 with respect to the feature GV. Gender assignment is both semantic
and formal. Therefore,
Timne gets 1 with respect to the feature AR. According to Wilson’s
description, Timne shows gender indexation on adnominal modifiers, pronouns, predicative expressions. In addition, in Timne, the Indefinite Stabilizer, which
is used with indefinite nouns in order to encode non-verbal predication (Wilson 1961: 11), also inflects for gender (this is labeled as “other” in my coding).
The language thus gets 1 with respect to IND. Gender
and number are encoded cumulatively on the indexing targets (CUM=1).
The source does not provide any kind of information about gender shifts, which are, however, rather common phenomena
in languages with similar gender systems. The features M1 and M2 cannot be documented
for Timne. To summarize, for Timne, the values assigned
to each of the metric features are: GV = 1; AR = 1; IND = 1; CUM = 1; M1 = –; M2 = – Since two features
are missing, the sum of the feature
values is in this case divided
by 4 [(1+1+1+1) ÷ 4]. The GCS of Timne is thus 1. 6. Discussion 6.1 Genealogical and areal biases in the distribution of
GCSs In appendix C, the GCSs presented in table 4 are visualized on the basis of genealogical units. The tables in appendix
C show that, in general, closely related languages tend to have the same or very similar GCSs. For instance, all the Berber languages in the sample have a gender complexity score of 0.69. This tendency towards intragenealogical homogeneity in the complexity of gender systems
further supports the idea that grammatical gender is a
chiefly stable feature in the history of language
families (see section 3.1). Nevertheless, outliers (i.e., languages that exhibit a GCS that is
exceedingly higher or lower than what found
among closely related languages) are attested
in the following genealogical units: Bantu, Chadic, Cushitic, Khoe-Kwadi, Eastern Nilotic, Semitic.
I suggest that, at least in some of such cases,
the distribution of the outliers can be accounted for by taking
into consideration aspects of the social history of the
speech communities in question (e.g., geography, number of speakers, number of contact languages, type of language
contact, bilingualism, multilingualism). This is however
only a preliminary suggestion, which would need to be investigated further in what goes beyond the scope of the present study. Out of 84 languages, 18 scored 1, with all these being either
Bantu, North- Central Atlantic
or Mel. Typically, the gender systems of the Bantu and Atlantic type (i.e.,
North-Central Atlantic and Mel) exhibit features
of high complexity: high number
of gender distinctions, pervasive gender
indexation, manipulability of gender assignment, which is used to express variation in the countability properties of nouns and/or
in the size of the noun phrase
referents. Those Atlantic
and Bantu languages which rank lower than 1 in table 4 have gender systems in which one or more of the above-mentioned features has/have been either weakened
or lost. For instance, in 8 of the 23 Bantu languages
in the sample – Bafia,
Eton, Northern Sotho, Shona,
Swati, Tswana, Venda, Zulu – diminutive and augmentative suffixes have grammaticalized
from nouns. Of these eight languages, only Venda and, to a lesser extent, Shona combine the use of the diminutive and augmentative suffixes with the uses
of the dedicated diminutive and augmentative genders that are
characteristic of many Atlantic-Congo languages.[23] In the remaining
six languages, the evaluative genders
have been lost. As a result,
the complexity of the gender systems of these languages is lower than what found in other closely
related languages. Two outliers with respect to the Bantu and Atlantic
type of gender
system are the Bantu languages Kinshasa
Lingala (GCS = 0.22)
and Bila (GCS = 0.16).
My coding for Kinshasa
Lingala, the variety
of Lingala spoken
in the area of the capital city of the Democratic
Republic of Congo,
is based on Bokamba (1977) and Meeuwis (2013). Kinshasa Lingala preserves the system of noun class marking
which is typical of Bantu languages only on nouns. Meeuwis (2013) rightly refers to this set of singular/plural pairs of nominal
prefixes as inflectional classes: diachronically, they are a relic of the former Bantu-like gender system, but, synchronically, they merely function
as markers of nominal number. The Third Person Pronouns and
the Subject Prefixes index the animacy of the noun phrase referent.
Based on this account, I classify
Kinshasa Lingala
as a language with two genders
(Animate and Inanimate), semantic
gender assignment and two domains of gender indexation (pronominal and predicative). Compared to Makanza Lingala, the northwestern variety of Lingala whose origins go back to the language
standardization policies operated by the Scheutist missionaries between
1901 and 1902,
and which exhibits
a more conservative gender
system, the gender system of Kinshasa
Lingala is massively reduced.
According to Meeuwis (2013: 26), Kinshasa Lingala is the oldest variety and the direct descendant of the Bangala pidgin, which was originally spoken in the Bangala state post (on the northwestern banks of the Congo River) and later on
spread northeastward.[24] This variety resisted to the grammatical reforms introduced by the Scheutists, and
soon gained both native and second language speakers.
The pidginization process from which Lingala originated, as well as the highly multilingual ecology in which the Kinshasa
variety developed and expanded, can reasonably explain the patterns of simplification and reduction in the domain
of grammatical gender
that differentiate this variety from other Bantu languages, on the one hand, and from the standardized variety introduced by the missionaries in the northwestern areas of the Democratic Republic of Congo (Makanza Lingala), on the other
(on this account,
see also Bokamba 1977, 2009). Similarly to Kinshasa Lingala,
Bila has only two genders (the Animate and the Inanimate Gender), semantic
assignment rules and poor gender indexation. Differently from Kinshasa Lingala, however, gender
indexation in Bila is exclusively internal to the noun phrase
and limited to the domain
of adnominal modifiers (Kutsch
Lojenga 2003: 462). Bila is spoken in the northeastern part of the Democratic Republic
of Congo, which is also the northernmost corner of the Bantu-speaking area. The northern
part of the Bantu-speaking area is often described as a true borderland between linguistically very diverse communities that have extensive
contact with each other. In this area, Bantu speakers
are surrounded by speakers of Nilo-Saharan and Ubangi languages (Kutsch
Lojenga 2003: 451-452). Due to intense
mutual contact, both the Bantu and non-Bantu languages spoken in this area are characterized by massive lexical
borrowing as well as by grammatical innovations that are not shared with the respective cognate
languages outside the area. The reduced
gender system of Bila and other
neighboring Bantu languages is one of such area-specific features. The Semitic languages provide another
interesting illustration of a set of genealogically related languages with non-homogeneous GCSs. The highest
ranking GCSs within the Semitic
sample go to Maltese ( 0.78) and Amharic (0.67). Moroccan Arabic,
Standard Arabic and Tigre have the same complexity score,
0.61. The lowest ranking gender system is found in Hebrew (0.45), whereas Lisha´n Dida´n scored 0.53. Interestingly, the highest GCS, 0.78, is scored by Maltese, the Semitic language that stands out for its peculiar history
of long-term contact
and bilingualism with English, on the
one hand, and Romance languages (Italian and Sicilian), on the other. A similarly
high GCS goes to Moroccan
Arabic, a dialect of Arabic whose history is also characterized by long-term intense contact with Berber languages,
French and Spanish (for a case study of complexity of verbal inflection in Moroccan Arabic and other varieties of Arabic, see Kusters 2003). Finally, the history of Modern (Israeli) Hebrew is also intertwined with intricate sociolinguistic dynamics involving
processes of creolization, language shift and massive borrowing (see, among others,
Doron 2015; Zuckermann 2009). Two additional examples of outliers are Dahalo, with respect to the other
Cushitic languages, and Kwadi, with respect to the Khoe-Kwadi group. Dahalo has a GCS of 0.28, and its gender system has been described
by Tosco (1991: 20) as dying out as a result of contact with neighboring Bantu languages. Too little is known about Kwadi, a now extinct
language of Angola.
Gu¨ldemann (2004) describes its gender system as sex-based and pronominal, but not much information is given about mechanisms of gender assignment nor about the use of gender shifts to encode
diminutive and augmentative meanings
(which is well documented in all the other Khoe-Kwadi languages of the sample). Finally, the two lowest ranking
languages in the complexity rank given in table 4 are the Chadic languages
Mwaghavul (GCS = 0.08) and Pero (GCS = 0.12), both of which are spoken in Nigeria. The two languages also qualify as outliers with respect to the other Chadic languages in the sample.
Mwaghavul scores 0 with respect
to all the features of the complexity metric
except for CUM, for which the score is 0.5. There are two genders in Mwaghavul (Masculine and Feminine), gender assignment is semantic and gender indexation is only pronominal. Finally, there seems to be no possibility of manipulating gender
assignment in the language. With respect
to the cumulation parameter, Mwaghavul shows at least some patterns of interaction with number on the indexing targets. The Third Person Human Anaphoric Subject and Object Pronouns encode gender and number cumulatively. On the other
hand, the Third
Person Non-human Pronoun,
nɘ̄ , encodes neither gender nor number distinctions (Frajzyngier & Johnston 2005). A similar type of system is found in Pero even though,
from the description provided by Frajzyngier (1989), it is not entirely
clear what type of assignment rules the language
has and whether gender assignment
is rigid or manipulable. The remaining four Chadic languages
in the sample have higher GCSs (between 0.62 – Lele – and 0.45– Gidar).
The language-internal and/or socio-historical factors that might account for this distribution should
be further investigated. To summarize, in the languages
of my sample, complexity in the domain
of grammatical gender
tends to be replicated across
genealogically related languages. On the other hand, multilingualism, (long term and short term) language contact and second
language learning may be seen as possible disturbance factors that
introduce variation
(both in the form of simplification and complexification) in the gender system
of a language as opposed
to its closest relatives
(see also discussion in Trudgill 1999; McWhorter 2001). A systematic account of the effects of sociolinguistic and ecological variables on the complexity of gender falls outside the scope of this paper. In section 7, I put forward
a few suggestions on how various aspects of language
ecology could be implemented in the study of the grammatical complexity and stability of gender. 6.2 Interdependencies between sets of features: GV and
AR, AR and IND On the basis of the results
presented in table 4 an interesting relationship can be observed between
the features GV and AR, and AR and IND. Strictly semantic systems of gender assignment are only found
in 8 of the 84 gendered languages
within the sample:
Bila (Bantu), Dahalo
(Cushitic), Dime (South Omotic), Dizin (Dizoid), Kinshasa
Lingala (Bantu),
Koorete (Ta-Ne-Omotic), Masai (Eastern Nilotic),
Mwaghavul (Chadic).
All these languages
have two gender distinctions, and all but Bila and Kinshasa Lingala have sex-based
gender. Within my language
sample then, strict semantic gender assignment is only found in
languages with two or a maximum of three gender values.
Moreover, there seems to be a preference for strictly
semantic gender assignment in African languages to be based on cognitively basic oppositions such as human vs. non-human, male vs. female, animate vs. inanimate. It would
be interesting to investigate what type of preferences exist, if they exist, in areas
of the world where strictly semantic
gender assignment is more common. Finally, it is worth mentioning that the eight
languages of my sample with strictly semantic gender
assignment all score less than 1 with respect to IND: thus in none of these languages is gender indexation maximally pervasive.
These results are in line with a suggestion
that was put forward by Audring (2009) with respect to the relationship between pervasiveness
of indexation and type of assignment rules. Audring analyzes the assignment rules
of a number of pronominal gender systems from different
areas of the world, and considers aspects of the diachrony
of gender in English and Dutch. She shows that pronominal gender systems – where manifestations of gender throughout the discourse are rather poor – display a strong preference towards strictly
semantic assignment rules.
Within my language sample, only Mwaghavul (Chadic)
has pronominal gender and semantic assignment. However, the remaining
five languages with strict semantic
assignment score either 1/3 or 2/3 with respect to IND. In line with the expectation voiced in Audring (2009, 2014), these results suggest that when strict semantic
gender assignment is found in non-pronominal gender systems, gender indexation is still not maximally pervasive. In other words, semantic assignment
seems to generally tolerate lower amount of formal marking. 6.3
Some features may be stronger predictors of gender complexity than others As discussed in section 2.2, a major issue when investigating grammatical complexity
is how to quantify the contribution that the individual features
of a metric bring to the overall complexity score (what Miestamo 2006b, 2008 refers to as the problem of comparability). Given that it is extremely difficult to measure
the relative weight of the individual features
of a complexity metric,
as well as to establish the number and type of features to be included
in a metric, complexity metrics cannot be interpreted as uncontroversial and exhaustive
measurements, but rather as tools to detect
and describe tendencies in the complexity of a grammatical domain with respect
to a selection of relevant features
(for a similar discussion in a study of complexity in nominal
plural allomorphy, see also Dammel & Ku¨rschner 2008). I would like to suggest
here that one way of indirectly investigating the behavior
of a complexity metric is to correlate the individual features with each other. In order to do so with my own metric, I calculated the Squared Spaerman
rank correlation coefficients between the individual features of the
metric. The results are represented in the graph in figure 3. Figure 3 is organized as follows.
The individual features of the metric are displayed
both horizontally and vertically. In this way, correlations coefficients
between pairs of features can be read both row-wise and column-wise. Correlation coefficients are visualized
according to a color scale whereby white stands for no correlation
and gray for high correlation. The gray diagonal
area that cuts across the two halves of the figure represents correlation coefficients between
pairs of the same features (that is, CUM with CUM, M2 with M2, etc.). These gray boxes correspond to a correlation coefficient that equals to 1 since each feature obviously has the highest correlation with its own copy. These results are thus not relevant to the analysis. With respect
to correlations between
pairs of different features, the figure shows that the highest correlation coefficients are found between IND and M1 (= 0.353), GV and IND (= 0.295)
and GV and M1 (= 0.261). Figure 3: Correlation coefficients
between the features
of the metric The correlation coefficients between IND and M1, and, to a lesser extent, between GV and M1 can be interpreted as follows.
In the languages of my sample, the possibility of manipulating gender
assignment to encode
variation in the countability properties of nouns goes hand in hand with the presence of very pervasive gender indexation or, to a lower degree, high number of gender values. M1 is not widely distributed across the language
sample. It is only found
in Bantu (with the exception of Bila and Kinshasa Lingala), North-Central Atlantic, Berber, a subset of the Semitic languages, and in the Eastern Nilotic language Turkana. In a way then, both the distribution of M1 and its correlation coefficients
with IND and GV suggest that M1 is a very special property of gender systems,
which can only be found in systems with a high amount of formal marking
(IND) and/or a high number
of gender distinctions (GV). On the contrary,
the results show that M2, that is, manipulation of gender assignment to express
diminutive and augmentative meanings, has extremely
low correlation coefficients
with both IND and
GV as well as with all the other features of the metrics. As mentioned above, GV and IND exhibit a relatively
high correlation coefficient, 0.295. This result supports Audring’s (2014) argument, whereby a high number of gender
distinctions is likely to be found
in languages with pervasive indexation (see section 3.2). Moreover, figure 3 shows that AR has extremely low correlation coefficients with all the features of the metric.
These results might depend on the fact that only 8 of the 84 sampled languages
have semantic gender assignment. In other words, nearly all the languages of the sample
behave similarly with respect
to this parameter.
It would be interesting to investigate the behavior
of this feature in areas of the world where semantic
gender assignment is more frequent
and compare it with the results from Africa.
Finally, equally low correlations are found with the feature CUM. One question that is worth asking is whether the correlation coefficients
presented in figure
3 can tell us anything about
which of these
features is the best predictor of the GCS of each language. Since
the GCS is the averaged sum of the values that a language takes for each feature in the metric, the features
that show the highest
correlations with each other (M1, IND
and GV) can be expected to be those which also have a stronger
impact on the final score. This can be verified by examining
the associations between the independent variables (the features in the metric) and the dependent
variable (the GCS) in a purely descriptive way, that is, by stratifying our dependent variable, the GCSs, according to the potential predictors, the individual features in the metric (Harrell 2001: 125). This is
shown in figure 4. Figure 4: GCSs (Average) stratified according to feature
values Figure 4 is organized as follows.
The GCSs are displayed on the X-axis.
The left Y-axis represents
the values assigned to each feature in the metric; the right Y-axis shows the number of languages in the sample
where each of the feature values is found. The black dots represent
the mean of the GCSs that languages displaying a certain feature value have. For instance,
it shows that languages
that score 1/3 (0.3333333333) with respect to GV have a GCS which, on average, ranges between 0.6 and 0.8. The black dots thus allow us to see which of the features and feature
values can trigger the highest
GCSs in the languages
of the sample. As hypothesized based on the correlation coefficients
shown in figure 3, in the languages of my sample, the highest scores in GV, IND and M1, trigger higher GCSs. With respect to GV, the figure shows that the impact of the different feature values on the GCSs grows from 0 to 1/3, drastically drops at 2/3 and grows again at 1. This is likely to be an effect of the fact that only one language within
my sample has four gender distinctions, Ju|’hoan (Kxa). Ju|’hoan has a GCS of 0.36, which is one of the lowest scores in my language
sample. To summarize, even though
the quantitative analysis
applied to the data does not provide a solution
to the problem of comparability, it provides
valid tools for describing the behavior of the complexity metric
with respect to the data-set
investigated in this
paper. Provided that my metric is a good measure for (at least
some aspects of) gender complexity, the results suggest that GV, IND and M1 are the features which correlate more strongly with each other and which
seem to have the strongest impact on the final complexity scores of the languages of the sample. 7. Summary and concluding remarks The aim of this paper was to contribute to the debate on the empirical study of grammatical complexity by proposing a set of theoretical principles
and methodological tools that can be used to investigate the complexity of grammatical domains in a typological perspective. The study focused
on one grammatical domain, gender,
which was chosen in virtue of its well
known association with morphosyntactic complexity (inflection and indexation), diachronic stability and areal persistence. With respect to theoretical assumptions, linguistic complexity was here conceived of in terms of number of
parts/description length of a given
system. It was argued that typological complexity metrics should focus on individual grammatical domains and that the complexity of a given domain
should be evaluated against three principles: the Principle of One-Meaning–One-Form, the Principle of Fewer Distinctions, and the Principle of Independence. With respect to methodology, the study followed
a sampling procedure that exploits
areal and genealogical biases with the purpose of investigating if, and to which extent, typological distributions concerning
the complexity of gender systems are genealogically and areally entrenched. Finally, the study provided an empirical illustration of how complexity metrics may be designed and implemented quantitatively. This was done by expanding on the dimensions of gender complexity suggested
by Audring (2014) and converting
them into a set of features with measurable values.
Complexity scores for each of the sample languages
were then calculated on the basis
of a method introduced by Parkvall (2008). In section 7.1 and 7.2, I evaluate the main contributions of the investigation with respect to: (a) the complexity metric proposed and (b) the results obtained
in the study. 7.1 Evaluation of the complexity metric The metric designed
for this study consisted of six features and assessed
the complexity
of grammatical gender based on the following parameters: number of gender distinctions, gender assignment, patterns of indexation, interactions with two other nominal domains
– number and evaluative morphology – as reflected via gender assignment (manipulation of gender assignment) and type of exponence
of gender on the indexation targets (cumulation with number). The six features
are not to be understood as an exhaustive inventory of complexity parameters for gender, but as a first attempt
to translate a set of crosslinguistically documented properties of gender systems
into indexes of complexity. Here I make some suggestions about how the metric could
be further improved. First of all, the metric proposed
in this study does not include gender
marking on nouns (e.g., presence
vs. absence of overt gender, type of exponence
of gender on nouns) as one of the dimensions for assessing
the complexity of a gender system. This choice was motivated
by the idea that, in order to investigate the complexity of gender, one should
first look at the domain of encoding
that is most definitional of this morphosyntactic feature, i.e., indexation (there is no gender if there is no indexation). Nevertheless, understanding how overt gender marking on nouns affects
the overall complexity of a gender system is a promising
area to explore in further studies of the complexity of gender. One suggestion that is put forward by Audring (2016) is that, based
on the Principle of One-Meaning–One-Form (or Principle of Transparency in her own terminology), covert gender systems are more complex than overt gender systems
because in covert gender systems, nouns fail to mark a morphosyntactic feature
that they inherently carry. Second, further research is particularly needed to improve the analysis of gender indexation patterns.
In my metric, the amount of gender indexation per each of the sample languages
is established by counting the morphosyntactic domains in which gender marking
occurs in a language. As explained
in section 4.3, footnote 21, this is done by identifying the word classes that carry gender inflection and by ascribing them to one of five possible codings
for indexation domains (articles, other adnominal modifiers, predicative expressions, pronouns, and others). Thus feature IND provides a rough count of how pervasive gender indexation is in a language, but does not allow us to immediately verify whether, for instance, “one indexing
domain” means “only pronominal” or “only adnominal
modification”, or how many word classes inflect for gender within each of the relevant
domains (e.g., within the pronominal domain,
only personal pronouns
or personal pronouns
and demonstrative pronouns). Moreover, gender indexes are identified
on the basis of a set of distinguishable functions (e.g, modification in the case of adjectives, predication in the case of verbs etc.). Two functionally different indexes (e.g., definite articles and demonstrative
pronouns/modifiers) can have the same formal realization in one language. However, the metric does not account
for the implications of these patterns
of identity of forms on the complexity of individual gender systems. On a more general
level, accounting for the difference between gender systems in which the gender indexing
targets have the same formal realization and those in which indexing
targets are formally
distinct might be crucial, for instance, when investigating the relationship between complexity and difficulty in the domain of grammatical gender.
This line of research falls, however, outside
the scope of the present investigation. In addition, the metric does not directly account
for the frequency
of gender marking
in discourse, an issue that would be also worth exploring when examining
the relationship between
complexity and
difficulty. Finally, even though the metric allows for exploring
interactions between gender
and other nominal
features, the inventory of possible interactions is far from exhaustive, mainly because restricted to only two domains
(number and evaluative morphology). Further research
is needed on each of these
issues, whose relevance has also been recently discussed by Audring (2016 ). 7.2 Evaluation of the results and prospects for future
research The gender systems of the African
languages sampled for this study are generally associated with high degrees of complexity (see section 5). In addition,
the results show that the complexity of grammatical gender is likely to be replicated across genealogically related
languages. If these results are interpreted in terms of stability,
one could speculate
that, at least in this area of the world, not only are noncomplex gender
systems infrequent, but that they also represent diachronically unstable stages in the history of languages.
However, as discussed in section 6.1, some outliers were found in almost all the genealogical groupings
represented in the sample. In many such cases,
the outlier languages tend to stand out from closely related languages
because of rather
distinctive socio-historical factors: (1) high degree
of multilingualism/nonnative acquisition (e.g., Kinshasa Lingala and Modern Hebrew),
(2) intense long-term contact and bi- or multilingualism with
languages lacking gender
or displaying different types of gender
systems (e.g., Bila, Dahalo, Maltese). These results suggest that a grammatical feature like gender, which appears
to be rather stable when looking at genealogical and areal distributions at the macro-level, in fact exhibits
striking patterns of variation
when family-internal comparisons are carried out at the
micro-level. In this sense, the study
shows that investigating how related languages differ in complexity with respect to specific domains of grammar can be a promising way to explore the stability
of these domains. The results of the study also point to the necessity of integrating language ecology[25] in the typological study of the complexity of grammatical domains.
Only by implementing socio-historical factors
as variables of our complexity metrics can we explore the extent to which these factors contribute to grammatical complexification and/or simplification crosslinguistically. I would like to argue here that integrating language ecology in the crosslinguistic study of linguistic
complexity is central
to the development of sociolinguistic typology (Trudgill 2011) taken both as a method and a theory
of research on linguistic diversity (for a similar
approach to the study of the social determinants of linguistic complexity see also Lupyan & Dale 2010 and their Linguistic Niche Hypothesis, whereby the distribution of linguistic complexity is conceived of as due, at least in part, to the different social environments in which languages
are learned and used). By implementing methods
that systematically assess
the intersections between
ecological profiles and the complexity of grammatical domains,
an ecology-informed approach
to the typological study of linguistic complexity may also contribute to reducing, and ultimately overcoming, the gap between relative
and absolute approaches (see section 2.1). In conclusion, the metric
and the methodology proposed
in this study
are, in many respects, only a preliminary and far from exhaustive attempt
at assessing the complexity of grammatical gender
within and across
languages. Nevertheless I hope to have shown that this attempt is not just a sterile
exercise in determining how “rich” languages
can be with respect to a specific
domain of grammar, but rather a promising tool for exploring the distribution of linguistic diversity and understanding the internal and external
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40–67. Appendix A. The Language Sample Languages are listed
alphabetically. The language names are followed
by the ISO codes, and the names of the genealogical units that each language
is assigned to in Glottolog
(Nordhoff
et
al. 2013), as of September, 2015.
B. Complexity scores for the individual features
in the metric
Table 6 shows how each of the sampled
languages scored with respect to the features
of the complexity metric. Unlike in table 4, where the GCSs are rounded up to
numbers with two decimal places, unrounded figures are provided in table 6. The data are ordered
alphabetically based on the ISO codes of the sampled languages. See table 5
for the correspondent language names. Table 6: Complexity
scores
C. GCSs per genealogical units
In the following, GCSs are
visualized on the basis of genealogical units. The genealogical units that are
represented by one language only are not included in the appendix. These are:
Dizoid (represented by Dizi), Hadza (isolate), Kxa (represented by Ju|’hoan), Kwa (represented by SElEE), Sandawe
(isolate), South Omotic (represented by Dime), Tuu (represented by !Xoo). The GCSs of
these languages are given in table 4.
[1]The study presented in this paper is based on chapter 7 of my doctoral dissertation (Di Garbo 2014). The following
changes have been made: the theoretical assumptions behind the sampling methodology are now better clarified; the definitions of the three
principles that I use as guidelines for modeling
grammatical complexity have been improved;
aspects of the coding design and of the analysis
of the data have been revised.
Finally, the text has been completely rewritten. This research has been financially supported
by Stockholm University and, later on, by the Wenner-Gren Foundations postdoctoral mobility grant for the project: “Gender
systems, grammatical complexity and stability: A crosslinguistic study of language pairs”. I wish to thank Jenny Audring, O¨sten Dahl, Maria Koptjevskaja Tamm, Matti Miestamo, Mikael
Parkvall, Ljuba Veselinova, Bernhard
Wa¨lchli, for reading and commenting on previous versions
of this work, and Raphae¨l Domange, Thomas Ho¨rberg and Robert Östling for assistance with statistical analysis. I am also grateful to two anonymous
reviewers for their constructive comments
and to the editor of Linguistic Discovery, Lindsay Whaley, for assistance throughout the publication process.
Remaining errors and shortcomings are mine. [2] For the sake of clarity, the notion of linguistic complexity that I work with in this paper is in no way related to any type of judgment about how expressive a given language is. Showing that the grammar (or aspects of the grammar) of a language is (are) simpler than that (those) of other languages by no means implies that the former language is less efficient – from the point of view of communication – or more primitive than the latter. [3] The languages of each set are taken from the following groupings: Bantu, Germanic, Quechuan, Semitic. [4] Studies of language contact have shown that while short-term language contact between adult learners is likely to lead to simplification, long-term language contact that is characterized by pre-critical threshold multilingualism (i.e., child multilingualism) is likely to lead to complexification. For a general overview, see Trudgill (2011: chapter 2). [5] The question of how to model interactions between domains has already been approached in the literature on linguistic complexity. Dahl (2004: 46-50), for instance, uses the notion of choice structure to explain the selection of the value of a grammatical category (e.g., case) based on the syntactic context or the speech situation in which it occurs. This issue is also approached within the framework of Canonical Typology (see, for instance, Corbett 2012: 158). [6] With respect to number of case distinctions, and based on the Principle of Fewer Distinctions, the case system of Turkish would, of course, rank higher in complexity than the case system of German. This example is a clear illustration of the fact that, as pointed out in section 2.2, complexity evaluations can only have a local rather than global scope. [7] For a theoretical discussion of the term indexation as opposed to agreement see Croft (2003, 2013). [8] Nichols’ study is an attempt to test the equi-complexity hypothesis (see section 1) on a large sample of languages and based on a selection of features ranging from phonology to the lexicon. The study finds “no significant negative correlations between different components of grammar” (Nichols 2009: 119), which suggests that it is not possible to prove that complexity in one domain of grammar is compensated by simplicity in other domains. The equi-complexity is thus not supported by the data presented in the study. [9] The Koman language Uduk, spoken in Ethiopia, would seem to represent an exception to this otherwise universal tendency. In Uduk, semantics seems to play no role in gender assignment (for a description of the gender system of Koman, see Killian 2015). [10] The language sample
also contains languages such as Hebrew and Maltese, which
are actually spoken outside Africa. As Dryer (1989: 268) points out, all Semitic
languages can be seen as part of the same large linguistic area because “their
genetic relationships go in that direction”. [11] One of the most well-known and practiced methods of language selection in typology is the one designed by Dryer (1989). Dryer uses genera – i.e., genealogical units with time depth comparable to that of Indo-European subfamilies such as Romance or Germanic – as the basis for language selection. For an overview of sampling procedures in linguistic typology see Bakker (2011). [12] Nichols (1992: 25) defines
a stock as the “highest
level reconstructable by the standard comparative method.” [13] The genealogical relationships between the Omotic groups and their affiliation to Afro-Asiatic are still debated issues among specialists (for an overview, see Amha 2012). For instance, Glottolog (Nordhoff et al. 2013) classifies all the Omotic groups as independent groups outside Afro-Asiatic. In the table, I use Omotic as an areal cover term and follow the Glottolog classification for the individual subgroups. [14] As mentioned in section 3.2, gender systems with only semantic assignment rules are quite common crosslinguistically, whereas gender systems with only formal assignment rules are almost never encountered. [15] The choice of number and evaluative morphology as domains of analysis does not exhaust the whole range of nominal and non-nominal grammatical domains that gender can interact with (among which, for instance, case and definiteness). However, as shown in section 5, even though far from exhaustive, the metric proposed in this study is able to reveal a good deal of crosslinguistic variation with respect to the complexity of gender and can thus be considered a starting point towards more comprehensive models of interactions of gender with other domains and their effect on gender complexity. For an overview of interactions between domains also involving gender and other systems of nominal classification, see Aikhenvald & Dixon (1998). [16] Polarity phenomena, whereby polar opposites
within a gender and number
inflectional paradigm (e.g.,
masculine singular and feminine plural)
have the same type of encoding, do not count
as an instance of M1, as defined in this paper. In languages that exhibit polarity (e.g., the Cushitic
language Somali), the gender shifts that occur between singular and plural depend on
paradigm-specific patterns of exponence and syncretism (often restricted to only a subset
of indexing targets),
which do not affect
the semantic and pragmatic construal of the noun phrase referent with respect to its countability and quantifiability properties (as it happens
instead in those languages that I classify as instances of M1). For a discussion of polarity patterns
in Somali, see Corbett 1991: 195-197. [17] The following abbreviations are used in the glossed examples: F = feminine; M = masculine, SG = singular, PL = Plural. The glossing of the examples conforms to the Leipzig Glossing Rules: http://www.eva.mpg.de/ lingua/resources/glossing-rules.php [18] A distinction can be made between languages with dedicated diminutive and augmentative genders (as in the Bantu languages), and languages in which there are no diminutive and augmentative genders, but gender shifts between, say, masculine and feminine, are used to encode diminutive and augmentative meanings (as in the Berber languages). This distinction, and its relevance for gender complexity, are not directly addressed by my metric. However, it can at least be observed that, within the sample, languages with dedicated diminutive and augmentative genders are languages with a high number of gender distinctions, which score high with respect to feature GV (see table 2). The relationship between presence of dedicated diminutive and augmentative genders and gender complexity would deserve to be further investigated. [19] On the relationship between syncretism and cumulative exponence in the domain of case and number inflection, see the studies by Carstairs (1987) and Carstairs & Stemberger (1988). [20] The cut-off point for feature GV is in accordance with the coding
conventions for number of gender distinctions proposed by Corbett (2013a). [21] The coding for feature IND is based on the number of morphosyntactic domains that exhibit gender inflection in a given language. The cut-off point was set at four based on a convenience choice of four main domains of gender inflection: (1) articles (definite/indefinite articles), (2) other adnominal modifiers (including adjectives, demonstrative and possessive modifiers, numerals, quantifiers), (3) predicative expressions, (4) pronouns (including personal pronouns, demonstrative pronouns, possessive pronouns, relative pronouns). This choice was made based on documented crosslinguistic tendencies in the distribution of types of gender indexing targets, as well as in partial overlap with the Agreement Hierarchy proposed by Corbett (1979, 1991, 2006). To these four domains of gender inflection, a category “other” was added in order to account for less prototypical indexing targets (e.g., conjunctions). Within each of the four indexation domains, several word classes may exhibit gender inflection (e.g., personal pronouns and demonstrative pronouns within the pronominal domain); this is not directly addressed by the metric (even though it has been accounted for during data collection). The coding proposed in this study is, of course, only one of the possible ways to model complexity in the domain of gender indexation. For a discussion, see section 7.1. [22] In this paper, I use capital letters to refer to language-specific categories (e.g., the Neuter Gender in Turkana) and lowercase letters to refer either to a specific marker within a language or to grammatical domains as objects of crosslinguistic comparison (e.g., adjectives and pronouns in Turkana). [23] Diminutive and augmentative genders (counted as instances of M2 in my metric) are a distinctive property of the gender systems of many Atlantic- Congo languages. For a description and typological classification of the morpho-syntactic and semantic properties of the evaluative genders in Atlantic-Congo languages and beyond, see Di Garbo 2014, chapter 6. [24] Bangala is, in turn, the descendant of Bobangi. Bobangi was originally a trade river language spoken on the western part of the Congo River. When the Europeans started using it as a medium of communication, Bobangi underwent a process of massive pidginization with substantial influences from European languages and (mainly) western African languages. The Europeans spread the use of pidginized Bobangi outside its original territory and imposed it as a language of communication in the Bangala Station, where Bangala developed. For more details on the origins and spread of Lingala see Bokamba (2009); Meeuwis (2013). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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