A new approach to gender and classifier systems: Evidence from Austronesian and Papuan languages

A new approach to gender and classifier systems: Evidence from Austronesian and Papuan languages

Austronesian and Papuan Languages and Linguis<cs, SOAS, London 14 May 2016

Sebas<an Fedden & Greville G. CorbeL University of Sydney & Surrey Morphology Group

With thanks to the AHRC (UK)

Seman<cs

S P D

Form

S 1 1 2

P 1 ? 2

D 1 2 2

Tim Feist

Outline

•  DEFINING GENDER AND CLASSIFIERS: THE USEFULNESS OF CANONICAL TYPOLOGY

•  MORPHOSYNTACTIC FEATURES

•  ESTABLISHING GENDER VALUES

•  MIAN: A TEST CASE

•  CONCURRENT SYSTEMS: OUR TYPOLOGY •  CONCLUSIONS

2

1. The challenge of defini<on

•  Defini<on and descrip<on of nominal categoriza<on systems is s<ll difficult

•  Progress needed to make possible meaningful comparison across languages

3

... and specifically

•  Some familiar systems show peculiar combina<ons of elements

•  These combina<ons are not necessary nor should they be defini<onal

(Whether our favourite system is found in Archi, Cree, La<n, Tidore or Zulu, we need a broad view.)

4

Canonical Typology

•  Examine clusterings of proper<es (especially the problema<c ones): meaningful or coincidental?

•  Draw out the theore<cal space to tease apart the clusters

•  Use the canonical ideal as a baseline from which we measure the actual examples we find

CorbeL (2007, 2015); Brown, Chumakina & CorbeL (2013). And see the bibliography at: hLp://www.surrey.ac.uk/englishandlanguages/research/smg/canonicaltypology/bibliography/index.htm. Recent developments: phonology (Hyman 2012), phonaesthemes (Kwon & Round 2015)

5

Canonical Typology

•  The canonical ideal is defined –  from a specific set of criteria

–  that can be used to project the theore<cal space –  which is then populated by real instances

•  No<onal point where all criteria converge

•  Different from “typical”, “frequent” or “prototypical”

6

Criteria converging on the canonical

Canonical Typology

7

Relevance to gender and classifiers

•  Construing the two as extremes of an opposi<on has not proved fruikul

•  So we aLempt a defini<on of one extreme, namely canonical gender (CorbeL & Fedden 2017)

•  and we try it out on an intriguing language, Mian

8

2. Canonical morphosyntac<c features

•  The canonicity criteria are largely the same for: – gender – number – person – case

CorbeL (2007, 2012: 156-‐199)

9

Principles for canonical features and their values

I: Canonical features and their values are clearly dis<nguished by formal means.

II: The use of canonical morphosyntac<c features and

their values is determined by simple syntac<c rules. III: Canonical morphosyntac<c features and their values

are expressed by canonical inflec<onal morphology.

(CorbeL 2012: 155-‐199) 10

Canonical morphosyntac<c features

•  Logical problem: If morphosyntac<c features were fully canonical, they would be iden<cal, no way of telling them apart

•  Solu<on: examine the interac<on of morphosyntac<c features with canonical parts of speech

•  In canonical parts of speech (CorbeL 2013: 52; Spencer 2005: 102) the seman<cs, syntax and morphology align

•  For example, a canonical noun would –  denote an en<ty –  head a nominal phrase –  take the appropriate inflec<onal morphology

11

Canonical parts of speech and canonical morphosyntac<c features

Feature: F1 F2

Value: v1 v2 v11 v12

Lexicon: l1 l2 l11 l12

Part of Speech: PoS1 PoS2

12 CorbeL (2013)

Four criteria: C-‐1. exclusiveness: •  a lexical item belongs to just one part of speech •  a value belongs to just one feature CorbeL (2013)


13

Criterion exhaus6veness: •  every lexical item of every part of speech has available all values of all features

•  (alterna<vely: every feature value applies to all lexical items)

(Thus if there is a number feature with the values singular and plural, then in the canonical situa<on every lexeme would have singular and plural available to it.)

14


C-‐3. open and closed: •  All classes are closed, except the class of lexical items.

(Canonical parts of speech have open membership.)

Canonical parts of speech and canonical morphosyntac<c features (3)

15

C-‐4. composi6onality: •  given the lexical seman<cs of a lexical item and a specifica<on of its feature values, the meaning of the whole is fully predictable.

16

Canonical parts of speech and canonical morphosyntac<c features (4)

Canonical parts of speech and canonical morphosyntac<c features: weakening

Feature 1: F1 F2

Value: v1 v2 v11 v12

Lexicon: l1 l2 l11 l12

Part of Speech: PoS1 PoS2 Part of Speech: PoS1 (e.g. noun) PoS2 (e.g. adjective)

17


•  Weakening of canonical exhaus<veness: nouns select one of the values (gender)

•  Gender is not a fully canonical morphosyntac<c feature, since controllers do not have all values available

•  but it is the canonical agreement feature, for this same reason, since it is a lexical feature

18

3. Agreement classes

A set of nouns is an agreement class if and only if all members of that set have the property that whenever (i) they have the same morphosyntac<c specifica<on and (ii) they occur in the same agreement domain and (iii) they have the same lexical item as agreement target

then their agreement targets have the same morphological realiza<on.

19

Relevance of Agreement Classes

•  Sources: Zaliznjak (1964), CorbeL (2012: 80-‐85) •  The basic idea: nouns are in the same agreement class iff – given the same condi<ons – they control the same agreement

Canonical Gender – Criterion 1

Canonical gender values match agreement classes (CorbeL and Fedden 2017[2015])

20

Non-‐canonical phenomena

•  Phenomena which linguists have named (common nouns, hybrid nouns, sub-‐genders, etc.) can be understood as devia<ons from the canonical ideal

•  For Mian, we will discuss in turn: –  non-‐canonical controllers –  non-‐canonical targets –  non-‐canonical domains

21

4. Why Mian?

•  Rela<on of gender and classifiers: –  an opposi<on? –  part of the same space?

•  Gender and classifiers are oren treated as mutually exclusive systems of categoriza<on

•  Mian has both (Fedden 2011)

22

Mian

•  Ok family (ok ‘water, river’) (Healey 1964) •  Trans New Guinea (Pawley 2005; Ross 2005; Wurm 1982)

•  Telefomin District, Sandaun Province, Papua New Guinea

•  Two dialects –  Eastern dialect has around 1,400 speakers (Fedden 2011) – Western dialect (aka Suganga) has around 350 speakers

23

Puta<ve subgroups within Trans New Guinea (from Ross 2005: 34)

141st parallel

24

4.1 “Gender” in Mian

•  M, F, N1, N2 in the 3rd person singular –  Targets: free pronouns, ar<cles, demonstra<ves, verb –  All finite verbs have subject agreement –  Object agreement with these only:

-‐e ‘hit, kill (IPFV)’ -‐fû’ ‘grab (PFV)’ -‐lò ‘hit, kill (PFV)’ -‐nâ’ ‘hit, kill (PFV)’ -‐ntamâ’ ‘bite (PFV)’ -‐têm’ ‘see (PFV)’ -‐temê’ ‘look at (IPFV)’

25

“Gender” in Mian

(1) ē unáng=o wa-‐têm’-‐Ø-‐e=be 3SG.M woman(F)=ART.SG.F 3SG.F.OBJ-‐see.PFV-‐REAL-‐3SG.M.SBJ=DECL ‘He sees the woman.’ (SF field notes) (2) ō naka=e a-‐têm’-‐Ø-‐o=be 3SG.F man(M)=ART.SG.M 3SG.M.OBJ-‐see.PFV-‐REAL-‐3SG.F.SBJ=DECL ‘She sees the man.’ (SF field notes)

26



27



28

Agreement target: ar<cle

a. naka=e ‘a/the man’ naka=i ‘(the) men’ b. unáng=o ‘a/the woman’ unáng=i ‘(the) women’ c. tóm=e ‘a/the stone’ tóm=o ‘(the) stones’ d. káawa=o ‘a/the steel axe’ káawa=o ‘(the) steel axes’

29

Mian gender values SINGULAR PLURAL assignment

MASCULINE =e =i males FEMININE =o =i females NEUTER 1 =e =o inanimates NEUTER 2 =o =o inanimates: loca<ons, body

decora<on, weather phenomena, illnesses, abstract nouns, some tools and weapons

•  All Mian genders are non-‐autonomous values (Zaliznjak 1973[2002]: 69-‐74)

•  Assignment is predominantly seman<c

30

4.2 “Verbal classifiers” in Mian

(3) nē memâalo fút=e 1SG now tobacco=SG.N1 tob-‐ò-‐n-‐i=a 3SG.LONG.OBJ-‐take.PFV-‐SS.SEQ-‐1SG.SBJ=MED ‘Now I take the tobacco leaf and then I ...’ (Rolling smokes)

Dried Nico9ana leaves bound together

Source: SF fieldnotes

31

“Verbal classifiers” in Mian

•  Prefixed to verbs of object handling and movement, e.g. ‘give’, ‘take’, ‘put’, ‘throw’, ‘lir’, ‘turn’, ‘fall’ (about 50 verbs)

•  Absolu<ve basis

32

“Verbal classifiers” in Mian SINGULAR PLURAL assignment

M-‐CLASSIFIER dob-‐ dol-‐ males (M); plate, clothes, mosquito net, some bananas, some pandanus (N1)

F-‐CLASSIFIER om-‐ dol-‐ females (F); inanimates: all nouns of N2 gender

LONG tob-‐ tebel-‐ inanimates: arrow, pen, tobacco leaf, bone, tongs, bush knife, nail, belt (all N1)

COVERING gam-‐ gemel-‐ skin, palm bark, blanket (all N1) BUNDLE gol-‐ gulel-‐ string bag, bundles (all N1) RESIDUE ob-‐ ol-‐ tortoises (F); rest of inanimates (N1)

Strongly based on seman<cs, BUT assignment is less seman<cally transparent than in the gender system 33

4.3 Comparing the two systems

•  Criteria for canonical gender –  controllers –  targets –  domains

34

Controllers

•  In a canonical gender system, each controller has a single gender value

•  Source of non-‐canonicity: Common gender nouns, e.g. Mian éil ‘pig’, =l ‘dog’, and about a dozen others –  wan=e do-‐fâ! ‘Put down the male bird!’ –  wan=o om-‐fâ! ‘Put down the female bird!’

=> This affects both systems in a similar way

35

Assignment

•  The canonical gender assignment rule is seman<c

•  Gender can be read off the lexical informa<on (i.e. the seman<c specifica<on of the noun)

•  Principle of Canonical Typology: In canonical systems everything lines up

=> THE GENDER SYSTEM IS CLOSER TO THE CANONICAL IDEAL

36

Assignment

•  This goes against a widely held belief that gender “should” be opaque; but that’s when it’s easier to spot, but not when it’s canonical

37

Recategoriza<on

(4) som=e banana_bunch=SG.N1

dob-‐meki-‐n-‐e=be 3SG.M_CL.OBJ-‐hang_up.PFV-‐REAL-‐3SG.M.SBJ=DECL ‘I've hung up the banana bunch.’

38

(5) som=e banana_bunch=SG.N1

gol-‐meki-‐n-‐e=be 3SG.BDL.OBJ-‐hang_up.PFV-‐REAL-‐3SG.M.SBJ=DECL

‘I've hung up the banana bunch (that is supplied with some string or rope to hang it up on).’

Recategoriza<on

•  In a canonical gender system, nouns select one value •  Recategoriza<on -‐ source of non-‐canonicity

MIAN •  Gender system doesn’t allow this •  Classifier system does

=> the gender system is closer to the canonical ideal

39

Targets

•  Canonical features and their values are dis<nguished consistently across relevant parts of speech

•  Intui<on: The more evidence for gender the more canonical the system (“the more targets the beLer”)

MIAN •  Gender system targets: ar<cle, verb, pronoun •  Classifier system target: verb (subset only)

=> the gender system is closer to the canonical ideal

40

Syntac<c domains

•  Related to the intui<on about targets is the following: “The more syntac<c domains the beLer.”

•  Gender system domains: NP (ar<cle), clause (verb), extra-‐clausal (free personal pronoun)

41

Syntac<c domains (6) Futaman mín=e baa-‐n-‐e=o=le

Fu.valley son=ART.SG.M say.PFV-‐SEQ-‐3SG.M.SBJ=N2=TOP ībo wan-‐ībt=e 2PL who-‐2/3PL.AN.EMPH=CQ ge baa-‐s-‐e=ta [intervening material] say.PFV say.PFV-‐DS.SEQ-‐3SG.M.SBJ=and […] ē as=o hà’-‐n-‐e=o=le […] 3SG.M firewood=ART.N2 break.PFV-‐REAL-‐3SG.M.SBJ=N2=TOP […] ‘The man from the Fu river valley said, “Who are you?” and … when he [i.e. the man from the Fu river valley] cut the firewood …’ (Fedden 2011: 530)

ē 3SG.M

42

Syntac<c domains

•  Gender system domains: NP (ar<cle), clause (verb), extra-‐clausal (free personal pronoun)

•  Classifier system domain: clause (verb), no system of free personal pronouns

=> THE GENDER SYSTEM IS CLOSER TO THE CANONICAL IDEAL

43

Inequality of values

•  In any canonical situa<on, mappings are one-‐to-‐one. Specifically for gender, controller genders map to target genders one-‐to-‐one

•  Any discrepancy here is a source of non-‐canonicity

44

Mian SG PL

M A C

F B C

N1 A B

N2 B B

Andi SG PL

I A A

II B B

III C C

IV D D

e o

i o

SINGULAR PLURAL

N2

F

M

N1

Target genders in Mian

CorbeL (1991: 198)

SINGULAR PLURAL Assignment MASCULINE =e =i Males FEMININE =o =i Females NEUTER 1 =e =o Inanimates NEUTER 2 =o =o Inanimates: loca<ons, etc.

45


•  Mismatch between controller and target genders

•  No such mismatch in the classifier system => THE CLASSIFIER SYSTEM IS CLOSER TO THE CANONICAL IDEAL

46


G C

Common gender

Calibra<on

47

G C

G

C

Common gender

Assignment

48

G C

G

C

G C

Common gender

Assignment

Recategoriza<on

49

G C

G

C

G C

C

Common gender

Assignment

Recategoriza<on

Targets

G

50

G C

G

C

G C

G

C

Common gender

Assignment

Recategoriza<on

Targets

G

C Domains

51

G C

G

C

G C

G

C

Common gender

Assignment

Recategoriza<on

Targets

G

C Domains

G C Inequality of values

52

Summary •  Specifically on Mian:

–  a fairly canonical gender system –  a second system further away from the canonical ideal (but not

for all criteria)

•  More generally: –  noun categoriza<on systems are not discrete –  they have components which are more or less close to a

canonical ideal

•  Canonical approach –  makes possible a typology which can incorporate the

intermediate cases we find in the languages of the world

53

5. Concurrent systems

•  Challenge of languages with two or more systems of nominal classifica<on

•  Accounts oren simply assume that a given language indeed has two or more classifica<on systems

•  To make progress, we need to step back and show that a language really has dis<nct systems

... but this ques<on is typically not even raised some excep<ons: •  Dahl (2000) •  CorbeL (2012: 174-‐185) •  Round & CorbeL (2015)

54

Our sample

•  Contains some 20 languages •  Larger than some linguists would have imagined •  Favouring languages which show at least prima facie evidence for two systems

55

Key ideas in our typology

(i)  the degree to which the seman<cs of the two (candidate) systems are orthogonal to each other

(ii)  the degree to which their means of realiza<on

are dis<nct

Note: seman<cs = gramma<cal meanings

56

Dialect A1: Canonically one system

•  Seman<cs: same

(7) un-‐i sester simpa<c-‐i (8) un-‐e cos simpa<c-‐e one-‐ANIM sister nice-‐ANIM one-‐INAN thing nice-‐INAN ‘one nice sister’ ‘one nice thing’

57

; form: same

i i e e

Dialect C3: Canonically two systems

•  Seman<cs: different

(9) un-‐i sester simpa<c-‐u (10) un-‐e cos simpa<c-‐u one-‐ANIM sister nice-‐AUG one-‐INAN thing nice-‐AUG

‘one nice (big) sister’ ‘one nice (big) thing’ (11) un-‐i sester simpa<c-‐o (12) un-‐e cos simpa<c-‐o one-‐ANIM sister nice-‐DIM one-‐INAN thing nice-‐DIM ‘one nice (small) sister’ ‘one nice (small) thing’

58

i

i

e

e

u u

; form: different

o o

Nine types (Fedden and CorbeL (under review))

Seman<cs

S P D

Form

S A1 B1 C1

P A2 B2 C2

D A3 B3 C3

S = same P = par<al overlap D = different

Tim Feist

59

How many systems?

Seman<cs

S P D

Form

S 1

P

D

Same seman<cs + same forms ! canonical single system

60

How many systems?

Seman<cs

S P D

Form

S 1

P

D 2 Different seman<cs + different forms ! canonical concurrent systems

61

How many systems?

Seman<cs

S P D

Form

S 1

P 1

D 1 2

Regardless of form, same seman<cs ! 1 system

62

How many systems?

Seman<cs

S P D

Form

S 1 2

P 1 2

D 1 2

Regardless of form, different seman<cs ! 2 systems

63

How many systems?

Seman<cs

S P D

Form

S 1 2

P 1 2

D 1 2

Given a par<al overlap in seman<cs, the forms are decisive

64

How many systems?

Seman<cs

S P D

Form

S 1 1 2

P 1 2

D 1 2

Given a par<al overlap in the seman<cs and one set of forms ! 1 system

65

How many systems?

Seman<cs

S P D

Form

S 1 1 2

P 1 2

D 1 2 2

Given a par<al overlap in the seman<cs and 2 sets of forms ! 2 systems

66

How many systems?

Seman<cs

S P D

Form

S 1 1 2

P 1 ? 2

D 1 2 2

But what if we have a par<al overlap in the seman<cs and a par<al overlap in terms of forms? 67

How many systems?

Seman<cs

S P D

Form

S 1 1 2

P 1 ? 2

D 1 2 2

Regardless of form, same seman<cs ! 1 system

Regardless of form, different seman<cs !2 systems

Given a par<al overlap in seman<cs, the forms are decisive

68

KILIVILA Canonically ONE system – Same seman<cs & same forms (our Type A1)

•  Austronesian (Papuan Tip Cluster) •  Trobriand Islands •  25,000 speakers •  Senr (1986, 1996, p.c.)

69

Kilivila

(13) Kilivila (Senr 1986: 69)

mi-‐na-‐si-‐na na-‐yu na-‐manabweta vivila

DEM-‐FEMALE-‐PL-‐DEM FEMALE-‐two FEMALE-‐beau<ful girl

‘these two beau<ful girls’

70

Kilivila: Type A1

•  Targets –  numerals –  adjec<ves (e.g. -‐manabweta ‘beau<ful’) –  demonstra<ves (all but one) –  one interroga<ve

•  Almost always iden<cal forms comparing across targets

•  Single system •  An almost perfect example of Type A1, a canonically one-‐system language

71

Other Type A1 languages

•  Lamnso (Grassfields, Southern Bantu; McGarrity & Botne 2001)

•  Ngan'gityemerri (Southern Daly; Reid 1997) •  Bora-‐Miraña (Witotoan; Seifart 2005, 2009)

72

MALI

Same seman<cs & par<al overlap in forms (our Type A2)

73

•  Baining (Papuan) •  New Britain (PNG) •  2,200 speakers •  Stebbins (2005, 2011)

Mali (candidate 1)

•  Noun classes: MASCULINE, FEMININE, COUNT NEUTRAL, DIMINUTIVE, REDUCED, FLAT, EXCISED, LONG, EXTENDED

•  Targets: –  anaphoric pronouns –  adjec<ves –  numerals (1-‐3) –  indefinite pronouns –  contras<ve pronouns –  demonstra<ves

76

Noun class assignment

•  Seman<c assignment for humans and animals •  Residue distributed across all noun classes

– Assignment of masculine, feminine and count neutral governed by complex principles (Stebbins 2005: 92-‐108)

– Remaining five noun classes based on a single seman<c criterion each (i.e. diminu<ve, flat, cut-‐off, long or extended)

77

Mali (candidate 2)

•  Gender: MASCULINE, FEMININE AND NEUTER •  Targets:

– possessive pronouns – verb

•  Seman<c assignment

78

Example of both candidate systems

(14) Mali (Stebbins 2011: 65) kama pepavēs ma asēgēvēs nge pe ART paper.FLAT.SG REL one.FLAT.SG 3N.PRS there ‘there is a piece of paper there’

79

Complete predictability of gender from noun class

80

Mali system matrix

! MASCULINE! FEMININE! NEUTER!MASCULINE! matka!‘older!brother’! ;! ;!FEMININE! ;! likki!‘younger!sister’! ;!COUNT!NEUTRAL! ;! ;! bang!‘!house’!DIMINUTIVE! ;! ;! uratini!‘small!basket’!!REDUCED! ;! ;! srēvēm&‘dwarf’&!FLAT! ;! ;! tēlēngves)‘leaf’!EXCISED! ;! ;! amēngigl(‘board’(!LONG! ;! ;! suchulvet!‘slender!post’!EXTENDED! ;! ;! aupia!‘valley’!!

81

One system collapses onto the other. Mali has nine noun classes, and exactly nine cells are filled

Many-‐to-‐one mapping

•  Extended no<on of ‘same seman<cs’

•  English´ example of many-‐to-‐one mapping: –  pronouns: he for male humans, she for female humans, it for the residue

–  only rela<ve pronouns: who for humans only, which for the residue

•  On the scale same-‐different, this counts as prac<cally the same

82

Mali: Type A2

•  Forms on respec<ve set of targets largely dis<nct, but some overlap –  for both sets of targets masculine is marked by /ka/ and the masculine plural (for humans) is /ta/ (Stebbins 2011: 44-‐46, 137)

•  Type A2 –  Seman<cs: same (noun classes collapse onto gender) –  Forms: par<al overlap

83

Other Type A2 languages

•  Tatuyo (Tucanoan; Gomez-‐Imbert 2007) •  Lao (Tai-‐Kadai; Enfield 2004, 2007; Fedden and CorbeL 2016)

•  La<n

82

FRENCH Same seman<cs & different forms (our Type A3)

•  Indo-‐European (Romance) •  France, Canada, Switzerland, etc. •  75 million speakers

83

PAUMARÍ Canonically TWO systems: Different seman<cs & different forms (our Type C3)

•  Arawan •  Brazil •  around 300 speakers •  Chapman & Derbyshire (1991);

Aikhenvald (2010)

84

Paumarí (candidate 1)

•  Gender system: MASCULINE vs FEMININE •  Targets:

–  demonstra<ves (ada ‘this (M)’, ida ‘this (F)’) –  some adjec<ves –  inalienably possessed nouns –  verbs

85


Possessed noun Adjec<ve Verb gender + dependent intransi<ve

gender + theme

gender + immediate

MASCULINE -‐na~-‐Ø/-‐i -‐na -‐ha -‐ra FEMININE -‐ni -‐hi -‐ja/-‐a

Paumarí gender agreement suffixes (based on Aikhenvald 2010; Chapman & Derbyshire 1991)

(17) Paumarí (Aikhenvald 2010; also cited in Aikhenvald 2000: 71) Voroni-‐’a-‐ha ada kahami. fall-‐ASP-‐TH.M this.M palm.tree(M, NON-‐KA) ‘The palm tree fell down.’

86


•  Seman<c core to gender assignment •  Certain types of nouns are clearly associated with a par<cular gender value

•  Others are more evenly distributed across the two genders

87


•  ka-‐ vs non-‐ka; prefix ka-‐ (some<mes a-‐, ko-‐ or ki-‐) vs no overt marker

•  Targets –  inalienably possessed nouns –  those adjec<ves that show masculine / feminine agreement –  verbs

88


(18) Paumarí (Aikhenvald 2000: 71) Ka-‐voroni-‐’a-‐hi ida ojoro. KA-‐fall-‐ASP-‐TH.F this.F turtle(F, KA) ‘The turtle fell down.’

Assignment is complex

–  no nouns deno<ng humans in the ka-‐ class –  non-‐ka-‐class contains humans and non-‐humans

89

Paumarí: Two systems (Type C3)

•  Masculine-‐feminine gender system PLUS human-‐non-‐human gender system

•  Both seman<cs and forms are different •  Example of Type C3, i.e. a canonically two-‐system language

90

Paumarí: Two systems (Type C3)

•  No predictability of values from one system to the other

•  The two systems are independent in that all four combina<ons are aLested (CorbeL 2007: 257)

MASCULINE FEMININE

ka-‐ kasi’i ‘crocodile’ ojoro ‘turtle’ non-‐ka-‐ kahami ‘palm tree’ arabo ‘land, ground’

Interac<on of systems in Paumarí

91

Other Type C3 languages

•  Michif (mixed language of Canada; Bakker & Papen 1997; Bakker 1997)

•  Ayoreo (Zamucoan; Ber<neLo 2009; Ber<neLo & Ciucci 2014)

92

RUSSIAN Different seman<cs & same form (our Type C1 )

93

•  Indo-‐European (Slavonic) •  Russia, etc. •  170 million speakers

NOT YET FOUND Different seman<cs & par<al overlap in forms (our Type C2)

94

•  More plausible than Type 7 (different seman<cs & same forms), which exists!

TOBELO Different seman<cs & par<al overlap in forms (our Type C2)

95

•  West Papuan •  North Halmahera, Indonesia •  30,000 speakers •  Holton (2014, p.c.)

Tobelo (candidate 1)

•  Gender system: MASCULINE vs FEMININE in the third singular, and HUMAN vs NON-‐HUMAN in the third plural

•  Target –  verb

96

Tobelo (candidate 2)

•  System of 16 numeral classifiers primarily for physical objects (no classifier for humans!)

•  Plus an ‘incipient human/non-‐human dis<nc<on’, whereby

‘[n]umeral predicates with human arguments employ the portmanteau pronominal prefix ya-‐, which derives from the third person human plural actor prefix yo-‐ and the third person undergoer prefix a-‐’ (Holton 2014: 94)

97

Tobelo: (almost) Type C2

•  Combina<on of yo-‐ with the undergoer prefix means that it is not a clear case of a Type C2

•  but it suggests that Type C2 may well occur

98

PNAR Par<al overlap in the seman<cs & different forms (our Type B3)

99

•  Austroasia<c •  North Eastern India •  approx. 400,000 speakers •  Ring (2015)

Pnar (candidate 1)

•  Gender system : MASCULINE vs FEMININE vs NEUTER •  Targets

–  free pronouns –  demonstra<ves –  numerals –  rela<ve clauses

•  Assignment –  animates according to biological sex –  inanimates in all three genders, nouns referring to abstract en<<es are consistently neuter

100

Pnar (candidate 1)

(22) Pnar (Ring 2015: 320) εm jap ka=wi ka=knthaj tmmεn have die F=one F=female be.old ‘an old woman died’

101

Pnar (candidate 2)

•  Two numeral classifiers: ŋut ‘(living) human’ vs tļli ‘non-‐human’

•  Classifiers are free forms and obligatory

102

Pnar: Type B3

Pnar system matrix (3x2, 5 actual combina<ons)

•  Systems largely orthogonal

Classifiers HUMAN NON-‐HUMAN

Gender

MASCULINE + + FEMININE + + NEUTER -‐ +

103

Orthogonality score

•  Measure of orthogonality

(cells filled – minimum cells filled) (possible cells – minimum cells filled) = (5 – 3) = 2 = .67 (6 – 3) 3

So Pnar is an example of Type B3 (with a higher orthogonality score) 104

NANTI Par<al overlap in the seman<cs & different forms (our Type B3)

105

•  Kampan branch of Arawakan

•  Peru •  450 speakers •  Michael (2008)

Nan< (candidate 1)

•  Gender: -‐n ‘animate’ vs. -‐t ‘inanimate’ •  Assignment is seman<c •  Agreement targets

–  subset of adjec<ves –  numerals –  quan<fiers –  existen<al verb

106

Nan< (candidate 2)

•  Gender (second system): i-‐ ‘masculine’ vs. o-‐ ‘feminine’

•  Agreement targets –  verb –  possessed nouns –  pronouns –  demonstra<ves –  very small subset of adjec<ves

•  A few dimensional adjec<ves (e.g. ‘large’) par<cipate in both systems

107

Nan< (candidate 2)

•  Assignment –  Humans and animals assigned according to sex –  All inanimates are feminine

108

Nan<: Type B3

•  Nan< system matrix (2x2, 3 actual combina<ons)

•  Seman9cs: mid-‐way between canonically one system (2 cells filled) and canonically two systems (4 cells filled)

•  Orthogonality score: (3-‐2)/(4-‐2) = .5 •  Forms are different => two systems •  Example of Type B3, with a mid orthogonality score

ANIMATE INANIMATE MASCULINE + -‐ FEMININE + +

109

MIAN Par<al overlap in the seman<cs & different forms (our Type B3)

110

•  Trans New Guinea, Ok •  Papua New Guinea •  1750 speakers •  Fedden (2011)

Mian (candidates)

•  G-‐system (gender system) •  C-‐system (verbal classifiers)

111

MASCULINE FEMININE NEUTER 1 NEUTER 2

M-‐CLASSIFIER man, boy, boar — sleeping bag, plate, mosquito net

—

F-‐CLASSIFIER — woman, girl, sow — house, steel axe, money (kina note)

LONG — — tobacco, ea<ng implement, bush knife

—

BUNDLE — — string bag (large), tobacco pouch, plas<c bag

—

COVERING — — blanket, band aid —

RESIDUE — tortoise, scorpion cassowary egg, plane, hat

—

Mian: Type B3

•  Mian system matrix (6x4, 9 actual combina<ons)

112

Mian: Two systems (Type B3)

•  Seman<cs show considerable overlap Orthogonality score for Mian: (9-‐6)/(24-‐6)=.17

•  Forms are different

•  Example of Type B3, with a low orthogonality score

113

Other Type B3 languages

•  Mba (Tucker & Bryan 1966; Pasch 1985, 1986; CorbeL 2012)

•  Tariana (Arawakan; Aikhenvald 1994, 2003) •  Baniwa of Içana (Arawakan, Aikhenvald 2007) •  Yagua (Peba-‐Yaguan; Payne 1986, 2007; Payne & Payne 1990)

•  Innu (Algonquian; Drapeau & Lambert-‐Bré<ère 2011) •  Khasi (Austroasia<c; Rabel-‐Heymann 1977; Temsen, ms.)

114

BURMESO Par<al overlap in seman<cs & par<al overlap in forms (our Type B2)

115

•  East Bird’s Head •  West New Guinea •  250 speakers •  Donohue (2001), CorbeL

(2012)

Burmeso (candidate 1)

•  Gender: six values (I-‐VI) •  Target

–  verb

116

Burmeso (candidate 2)

•  Gender (second system): six different values (MASCULINE, FEMININE, NEUTER, MASCULINE INANIMATE, FEMININE INANIMATE, NEUTER ANIMATE)

•  Target –  adjec<ve

117

Burmeso: Type B2

•  Burmeso system matrix (6x6, 16 actual combina<ons) agreement on adjec<ve

agreem

ent o

n verb

MASCULINE FEMININE NEUTER MASCULINE INANIMATE

FEMININE INANIMATE NEUTER ANIMATE

I 44 plus all male kin terms

5 (4 birds) 1 (‘neck’) 2 (‘sea’, ‘wound’)

II 7 plus all female kin terms

4 1 (‘small goanna’)

2 (‘sago rinser (lower)’, ‘string.shapes’)

III 3 28, mainly inanimate 10, inanimate 1 (‘goanna’)

IV 9, inanimate

V 2 (‘banana’, ‘sago tree’)

VI 1 (‘arrow’) 1 (‘coconut’)

118

Burmeso: Type B2

•  Orthogonality scores: (16-‐6)/(36-‐6) = .33 •  Par<al overlap in forms

–  Donohue (2001: 105) points out ‘the strong resemblances between the forms of the gender suffixes and the set II verbal class agreement prefixes’

•  Example of Type B2 •  Right in the middle of our canonical typology!

119

BAGVALAL Par<al overlap in seman<cs & same forms (our Type B1)

120

•  Nakh-‐Dagestanian •  Dagestan •  1,500 speakers •  Kibrik (2001)

ALested types

Seman<cs

S P D

Form

S A1

Kilivila B1

Bagvalal C1

Russian

P A2 Mali

B2 Burmeso

C2 {Tobelo}

D A3 French

B3 Mian

C3 Paumarí

S = same P = par<al overlap D = different

121

6. Conclusions •  Specifically on Mian

–  a fairly canonical gender system –  a second system further away from the canonical ideal (but not

for all criteria)

•  More generally –  noun categoriza<on systems are not discrete –  they have components which are more or less close to a

canonical ideal

•  Canonical approach –  makes possible a typology which can incorporate the

intermediate cases we find in the languages of the world

122

Conclusions

•  Nominal classifica<on systems –  there are languages with more than one system –  our typology can incorporate phenomena previously called “genders” and “classifiers”, and the intermediate cases we find in the languages of the world

–  we have offered a typology of concurrent systems of nominal classifica<on

123

THANK YOU

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CorbeL, Greville G. 2012. Features. Cambridge: Cambridge University Press CorbeL, Greville G. 2013. Canonical morphosyntac<c features. In: Dunstan Brown, Marina Chumakina & Greville G. CorbeL (eds), Canonical Morphology and Syntax, 48-‐65. Oxford: Oxford University Press.

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Dahl, Östen. 2000. Elementary gender dis<nc<ons. In: Barbara Unterbeck, Ma� Rissanen, TerLu Nevalainen & Mirja Saari (eds.) Gender in Grammar and Cogni9on: I: Approaches to Gender, II: Manifesta9ons of Gender (Trends in Linguis<cs: Studies and Monographs 124), 577-‐593. Berlin: Mouton de Gruyter.

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APPENDIX

FRENCH Same seman<cs & different forms (our Type A3)

•  Indo-‐European (Romance) •  France, Canada, Switzerland, etc. •  75 million speakers

132

French

•  Gender system: MASCULINE vs FEMININE; forms expressing these values depend on target

(15) le garçon est content l-‐ə gaʁso ɛ kõtɑ DEF-‐M.SG boy(M)[SG] is happy[M.SG] ‘the boy is happy’

(16) la femme est contente

l-‐a fam ɛ kõtɑ-‐t DEF-‐F.SG woman(F)[SG] is happy-‐F.SG ‘the woman is happy’

133

French: Type A3

•  Forms are phonologically different, but realize the same gender values (the same gramma<cal meanings)

•  Single system •  Example of Type A3

134

RUSSIAN Different seman<cs & same form (our Type C1 )

135

•  Indo-‐European (Slavonic) •  Russia, etc. •  170 million speakers

Russian

•  Candidate 1: MASCULINE vs FEMININE vs NEUTER •  Candidate 2: ANIMATE vs INANIMATE •  3 X 2 = 6 possibili<es

•  Large literature •  Evidence in a small part of the paradigm only (the accusa<ve case)

136

Animacy has no unique forms

!!! SINGULAR! PLURAL! !

FEMININE! NEUTER! MASCULINE! !!!NOMINATIVE! novaja!

novoe!novyj! novye!

!

ACCUSATIVE!INANIMATE!

novuju!!

ANIMATE! !novyx!

!

GENITIVE!

novoj!

! novogo! !LOCATIVE! ! novom! !DATIVE! ! novomu! novym! !INSTRUMENTAL! ! novym! novymi! !

!

The Russian adjec<ve novyj ‘new’: paLerns of syncre<sm

137

Animacy in Russian

(19) ja viž-‐u star-‐yj dom I see-‐1SG old-‐M.INAN.SG.ACC house(M.INAN)[SG.ACC] ‘I see an old house’

(20) ja viž-‐u star-‐ogo drug-‐a

I see-‐1SG old-‐M.ANIM.SG.ACC friend(M.ANIM)-‐SG.ACC ‘I see an old friend’

(21) ja viž-‐u star-‐ogo dedušk-‐u

I see-‐1SG old-‐M.ANIM.SG.ACC grandfather(M.ANIM)-‐SG.ACC ‘I see (my) old grandfather’

Similarly for the plural (all three genders) 138

Animacy: ac<on in the accusa<ve

139

BAGVALAL Par<al overlap in seman<cs & same forms (our Type B1)

140

•  Nakh-‐Dagestanian •  Dagestan •  1,500 speakers •  Kibrik (2001)

Bagvalal

•  Gender: MASCULINE vs FEMININE vs NEUTER •  Assignment is seman<c

–  humans according to biological sex –  rest is neuter

141

Bagvalal Bagvalal (Kibrik 2001: 64-‐66):

(23) waša w-‐iRi (24) waša-‐bi b-‐iRi-‐r a. boy M.SG-‐stop a. boy-‐PL HUM.PL-‐stop-‐HUM.PL

‘the boy stopped’ ‘the boys stopped’ b. jaš j-‐iRi b. jaš-‐i b-‐iRi-‐r

girl F.SG-‐stop girl-‐PL HUM.PL-‐stop-‐HUM.PL ‘the girl stopped’ ‘the girls stopped’

c. ʕama b-‐iRi c. ʕama-‐bi r-‐iRi

donkey N.SG-‐stop donkey-‐PL N.PL-‐stop ‘the donkey stopped’ ‘the donkeys stopped’

142

Bagvalal Bagvalal (Kibrik 2001: 64-‐66):

(23) waša w-‐iRi (24) waša-‐bi b-‐iRi-‐r a. boy M.SG-‐stop a. boy-‐PL HUM.PL-‐stop-‐HUM.PL

‘the boy stopped’ ‘the boys stopped’ b. jaš j-‐iRi b. jaš-‐i b-‐iRi-‐r

girl F.SG-‐stop girl-‐PL HUM.PL-‐stop-‐HUM.PL ‘the girl stopped’ ‘the girls stopped’

c. ʕama b-‐iRi c. ʕama-‐bi r-‐iRi

donkey N.SG-‐stop donkey-‐PL N.PL-‐stop ‘the donkey stopped’ ‘the donkeys stopped’

143

Bagvalal

•  Sex-‐based dis<nc<on operates only for humans

•  Seman<c opposi<ons suggest four possibili<es –  only three are reflected in the gender system

144

Bagvalal

Bagvalal system matrix (two feature analysis) HUMAN NON-‐HUMAN

MASCULINE + -‐ FEMININE + -‐ NEUTER -‐ +

145

Bagvalal

Bagvalal system matrix (one feature analysis)

MASCULINE + FEMININE + NEUTER +

146

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A new approach to gender and classifier systems: Evidence from Austronesian and Papuan languages

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