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Today

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key insight: networks are 'closer' to the individual than social classes. ... Social networks allow the investigagion of forces that impact individual ... – PowerPoint PPT presentation

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Title: Today


1
Today
  • Networks, Day 2 Milroy and Milroy, 1978
  • Social network as an analytical framework
  • Social network as a speaker variable
  • The Linguistic Consequences of Being a Lame

2
Social network as a speaker variable
  • We can quantify individual's informal social
    contacts
  • -- how many people in the community do they
    know?
  • -- how many of these also know each other?
  • -- in what capacities?
  • -- key insight networks are closer to the
    individual than social classes. They enable us
    to see the influences on the individual.
  • 4 principle indicators of a person's integration
    into a network
  • 1. neighborhood of residence (physical
    rootedness)
  • 2. kinship
  • 3. occupation
  • 4. voluntary association

3
Social network as a speaker variable
  • Class-based approaches ascribe group membership.
    Network approaches focus on individual agency
    (avowed membership)
  • voluntary association chosen modes of informal
    interaction in community "centers"
  • --the individual as a free agent
  • i.e., choice of interactions within the network
    play a crucial role in predicting linguistic
    behavior

4
Belfast, Ireland
  • Belfast Change and variation in an urban
    vernacular (Milroy Milroy, 1978)

5
Background 21970s Belfast
  • Influx of population following the potato famines
    of the 19th century
  • Belfast communities differed in recency of
    settlement

West Belfast East Belfast
Catholic Protestant
Recent arrivals Long-established community
Originated in Central, Southern Northern Ireland Originated in Ulster Scots-- East and West Ulster
6
Linguistic Variables
  • (a), (e), ( ), (ai), (th)

v
1 2 3 4 5 6 7 8
Variable (ai) 1-3pts EI eI night (a) 1-5pts bag bEg, butman mç.n (I) 1-3pts (v)1 U hut (th) O mother (v)2 v, pull (E)1 Q slept (E)2 Q in disylls.
7
Methods 1
  • Conducted an ethnography of the community
  • 1.) Position of the community in relation to the
    wider urban area
  • 2.) Network patterns within the community
  • 3.) Linguistic and non-linguistic norms
    governing face-to-face interaction
  • 4.) Characterization of sociolinguistically
    significant personality types
  • a. Oddballs
  • b. Insiders

8
Methods 2
  • Speakers drawn from 3 Core neighborhoods each
    working class, economically depressed
  • Clonard Hammer Ballymacarrett
  • West West East
  • Catholic Protestant Protestant
  • Under redevelopment Location of shipyard
  • 16 respondents
  • 2 genders
  • 2 age cohorts Young 18-25 Middle-aged 42-55
  • ----
  • 48 respondents

9
Calculating network strength2 Examples
  • For each condition met, the speaker was assigned
    1 point. Scores could range from 0 points (no
    conditions met) to 5 (total of 6 possible point
    values)
  • Paula Hanna
  • Large family, all residing locally No kin in the
    area no family of her own
  • Visits to neighbors are frequent Does not
    interact with neighbors
  • Belongs to a weekly bingo group Spends
    evenings/weekends at home
  • Cares for a disabled woman 2 miles
    watching TV
  • from the Clonard (on the Ballyma- Child of a
    Prot/Catholic mixed marriage
  • carrett side of the River Lagan) Works in the
    cafeteria of the Royal Victoria
    Hospital
  • Workmates are not from the Clonard

10
Calculating network strength2 examples, cont.
  • Scores on the Belfast network strength
    scale P H
  • 1. Membership in a high-density,
    territorially-based cluster 1 0
  • 2. Substantial kinship ties within the
    neighborhood 1 0
  • 3. Employed in the same place as at least 2
    others 0 0
  • 4. Workmates include members of the same
    gender 0 0
  • 5. Voluntary association with workmates 0 0
  • 2 0

11
2 Examples, cont.
  • Main finding linguistic variable scores turn
    out to be closely related to (i.e., to co-vary
    with) the variable of personal network
  • Scores assigned on 8 linguistic variables

1 2 3 4 5 6 7 8
Variable (ai) 1-3pts EI eI night (a) 1-5pts bag bEg, butman mç.n (I) 1-3pts (v)1 U hut (th) O mother (v)2 v, pull (E)1 Q slept (E)2 Q in disylls.
Hanna 1.4 1.05 1.2 0 0 0 66.7 25
Paula 2.4 2.63 2.5 9 58.34 70.48 100 47.83
12
Results 1
  • Speakers drawn from 3 Core neighborhoods each
    working class, economically depressed
  • Clonard Hammer Ballymacarrett
  • West West East
  • Catholic Protestant Protestant
  • Under redevelopment Location of shipyard

13
Results 1
  • Characterization of the communities showed that
    B, H, and C were characterized by dense
    overlapping kin and friendship networks that
    tended not to cross the territorial boundaries
    perceived by the residents.
  • Close-knit networks were maintained through
  • residents regular visits to each others
    homes
  • prolonged visits
  • corner hanging
  • common form of employment
  • local place of occupation (reinforcing
    traditional gender roles)
  • Why is this relevant? The degree to which people
    use vernacular speech norms seems to correlate to
    the extent to which they participate in
    close-knit networks.

14
Results 2
  • (a), (e), ( ), (ai), (th)
  • 1.) IS shows a shift away from casual speech or
    SS (expected)
  • e.g., (th)-deletion reveals that speakers who
    delete in SS do not delete at all when reading a
    wordlist
  • 2.) WLS scores closer to casual speech
    (unexpected), counter to predictions of social
    class model
  • e.g., Ballymaccarrett (ai) and Clonard ( ) defy
    the expected pattern

v
v
15
Results 3
  • cont.,
  • 3.) Participation in newer local changes
  • e.g., (a) Clonard females as innovatory shows
    stylistic variation as (th) does, however, WLS
    closer to the vernacular form than IS

v
16
Discussion Key findings of Social network studies
  • Fine grained-view of the relationship between
    speaker variables and linguistic variables,
    showing
  • individuals behavior (range of within-speaker
    variability)
  • the forces that impact individual behavior
  • Social networks allow the investigagion of forces
    that impact individual behavior better than
    social classes (they are better able to explain
    individual behavior)
  • Tightly-knit, territorially-based social networks
    are norm-enforcing mechanisms, leading to the
    conservation of vernacular norms (e.g., local
    dialect), and resisting pressures from the
    outside.
  • The degree to which people use vernacular speech
    norms seems to correlate to the extent to which
    they participate in close-knit networks. (Milroy
    and Milroy 1988185)

17
Other Studies
  • Marthas Vineyard, Labov (1963)
  • Reading adventure playgrounds, Cheshire (1982)
  • Detroit Black English Vernacular (AAVE), Edwards
    (1992)
  • Grossdorf, Lippi-Green (1987)

18
Applying the notion of the Social network
  • Hymes, 1974 reserves the notion of community for
    local units characterized for their members by
    common locality and primary interaction.
  • How might we define a local unit or
    pre-existing group?
  • pre-existing social cluster urban village,
    neighborhood cluster
  • Two approaches to quantifying social integration
    into a pre-existing group
  • 1. Milroy and Milroy network strength score
  • 2. Labov Sociometric diagram with reciprocal
    naming

19
Social networks Quantifying network strength
  • Boissevain, 1972 (anthropologist)
  • Social networks the web of social relations
    within which every individual is embedded.
  • points individuals
  • anchored to ego

20
Social networks Quantifying network strength
  • Boissevain, 1972 (anthropologist)
  • Social networks the web of social relations in
    which every individual is embedded.
  • points individuals lines social relations
  • anchored to ego

21
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Open vs. Closed
  • Dense
  • Multiplex

22
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Dense
  • Multiplex
  • In a dense network, a large number of persons to
    whom ego is linked are also linked to each other.

23
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Dense
  • Multiplex
  • In a dense network, a large number of persons to
    whom ego is linked are also linked to each other.

24
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Dense
  • Multiplex
  • In a dense network, a large number of persons to
    whom ego is linked are also linked to each other.

25
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Dense
  • Multiplex
  • In a multiplex network, ego interacts with other
    persons in multiple capacities, referred to as
    activity fields.
  • -- activity fields school, church, occupational,
    kinship, extracurricular, sports, politics,etc.

26
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Dense
  • Multiplex
  • In a multiplex network, ego interacts with other
    persons in multiple capacities, referred to as
    activity fields.
  • -- activity fields school, church, occupational,
    kinship, extracurricular, sports, politics,etc.

27
Social networks Quantifying network strength
  • We may characterize networks in terms of their
  • --structure (density) --direction of movement
  • --content (multiplexity) --frequency of
    interaction
  • Characterizations
  • Dense
  • Multiplex
  • In a multiplex network, ego interacts with other
    persons in multiple capacities, referred to as
    activity fields.
  • -- activity fields school, church, occupational,
    kinship, extracurricular, sports, politics,etc.

28
The Variable Rule Example
  • (-t/d)-deletion

t or d is variably deleted following a consonant
when following a morpheme boundary or preceding a
vowel.
t,d ? ltøgt / cons ltgt __ lt-sylgt
Grp 1 likelihood Grp 2 likelihood
98 1 93 1
64 3 61 2
81 2 19 3
24 4 16 4
Grp 1 likelihood Grp 2 likelihood
98 1 93
64 3 61
81 2 19
24 4 16
Grp 1 likelihood Grp 2 likelihood
98 93
64 61
81 19
24 16
(a) (KDMM)__K He ran past me. (b) (KDMM)__V He
ran past us. (c) (KDP)__K He passed me. (d)
(KDP)__V He passed us. ltgt variable form or
ordered constraint ????? morphological
constraints _MMmonomorphemic _Ppolymorphemic
phonological constraints _Kfollowing
consonant _Vfollowing vowel
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