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COMMUNICATION AND GENDER :PENCIL2: SPA2 LECTURE 8 - Coggle Diagram
COMMUNICATION AND GENDER
:PENCIL2:
SPA2 LECTURE 8
Non-verbal Communication
Judgement accuracy
Hall (1978)
- review of 75 studies (posed and spontaneous expressions):
24 significant differences
23 in favour of women (e.g. women more accurately judged emotion)
Hall (1984)
reviewed a further 50 studies:
11 significant difference
10 in favour of women
Expression Accuracy
Women typically encode more clearly:
Hall (1979)
– Review of 26 studies:
9 significant gender difference
8 in favour of women
Hall (1984)
– Review of further 17 studies:
9 significant gender difference
7 in favour of women
N.B.
Accuracy does not necessarily represent a social advantage
Importance of situational context (e.g. in Poker, you wouldn’t want to show your emotions - so it’s not always an advantage)
Channel Differences
Smiling:
LaFrance & Hecht (2000)
– Meta-analysis of gender & smiling (59,076 participants in 147 research reports):
Highly significant difference (mean effect size: d=.40) – women smile more
Age – most pronounced for the 18-23 year-old group (>13-17 > 24-64)
Cross-cultural comparisons – gender difference strongest for Caucasians:
African-Americans (significant)
Asians (but not significant)
Gaze:
Hall (1984)
– Review of 119 studies of gaze and gender:
Every study with a significant gender difference showed females gaze at others more
Results consistent with female advantage on tests of judgement accuracy
Explanations for Gender Differences
Social Power
If you’ve got less social power, it is more important to be able to read the nonverbal behaviour of the people.
If you have high power, you might not care!
Inverse relationship between power and decoding emotional cues.
Alternative view suggests that power comes from a form of greater composure and the ability to conceal, so thereby other people don’t know what a person is thinking or what they’re going to do, and this unpredictability can lead to quite powerful situations.
Socialisation (e.g. Accommodation)
Understanding what others seek to communicate
Making your own messages easy to understand
Women are socialised to be accommodating to other people
Gender differences in NVC can be explained through both approaches.
Gender differences in Speech
Gender, Language and Power
Lakoff (1973)
A language of “powerlessness”
Two features to be discussed:
Hedges
(mitigating devices which lessen the impact of an utterance)
Tag questions
(e.g., doesn’t it?, isn’t it?)
Hedges - "You know...'
Holmes (1986)
Study of functions of “you know”
Corpus of 50,000 words of speech - 25,000 words per gender.
Around 20,000 words were from formal conversations such as TV or Radio interviews.
The other 30,000 were from informal conversations - e.g. meal-time chats in private homes.
32 males, 32 females.
Over 200 uses of the specific hedge “you know”
Men and women used it for different functions:
(1) Certainty and conviction
(2) Doubt and uncertainty
Women made greater use of “you know” to convey
certainty
, men used it significantly more to convey
uncertainty
E.g. “You know I’m in charge.” vs. “I’m in charge, you know” has very different meanings.
N.B.
Exactly the
opposite
of what Lakoff would expect
Hedges - "I think..."
Holmes (1985)
Analysis of the functions of the term “I think”
Typically related to uncertainty.
(1) Deliberative form (
booster
)
(2) Tentative form (
hedge
)
E.g. “I think the film was wonderful!” vs. “The film was wonderful... I think.”
Women used “I think” more frequently as a
booster
than as a
hedge
; the reverse was true for men.
N.B
. Again, exactly the
opposite
of what Lakoff would expect
Tag Questions
Holmes (1985)
4 principal functions of tag questions:
(1) Convey uncertainty (acc. to Lakoff)
(2) Facilitate conversation
E.g. “You’ve got a new job Tom, haven’t you?”
(3) Confrontational
E.g. “Your work really better have improved, hadn’t it?”
(4) Soften the force of a criticism
E.g. “That was a daft thing to do, wasn’t it?”
Uncertainty tags – more by men
Facilitative tags – more by women
The
opposite
of what Lakoff would expect.
Women's language: Summary...
Hedges & tags used to convey uncertainty more by men
Opposite of Lakoff
Hedges & tags – other functions besides conveying uncertainty
Basic problem with Lakoff’s analysis:
The function of an utterance cannot be understood from an analysis of its linguistic form alone
In defence of Lakoff:
Link between language use, gender & power (Cameron, 2007)
Stimulated much research on gender & language.
Tag Questions
Cameron, McAlinden & O’Leary (1988):
Use of tag questions predicted better by social role than by gender:
Facilitative tags used by professionals
Information-checking tags used by audience members, pupils & callers
Powerful language
Interruptions
Zimmerman & West (1975)
Opposite-sex conversations:
Made covert recordings around a university campus of around 55 naturally occurring conversations between same-sex and opposite sex pairs.
Wouldn’t get past the ethics committee today!
Men typically interrupt
In both macro- (e.g. industry) and micro-institutions (e.g. conversation).
Murray & Covelli (1988)
Women interrupted men twice as often
Conducted at a similar time, where social change is unlikely to have occurred, it is unusual to find this polarization in findings.
Anderson & Leaper (1998)
Meta-analysis:
All interruptions (d=.15) [men>women]
Intrusive interruptions (d=.33)
Attempts to usurp the other person’s turn.
But findings heavily qualified by situational and contextual factors
Communication between "cultures"
Maltz & Borker (1982):
“Men and women differ in rules for interpreting language”
Different rules learned principally in same-sex groups (ages 5-15), e.g.,
(1) Interpretation of listener responses
“Hmmm’, “yeah”, “okay”, “mhm”
Female = means ‘I’m listening’
Male = I agree with what you’re saying (stronger meaning for males than for females)
(2)The meaning of questions
(3)Verbal aggression
This two-cultures view popularised by Tannen (1991) in “You Just Don’t Understand”
Responsible for much miscommunication
Evaluation of the Two-Cultures Approach
(2) Polarization
E.g., popular best-seller “Women are from Venus, Men are from Mars”
(Gray, 1995)
Aries (1996)
– Anyone is capable of displaying both “masculine and feminine styles of interaction”
Overlap between men and women
Differences not mutually exclusive
Style depends on other factors, e.g., status, role, goals, conversational partners, situational context
(3) The Myth of Mars and Venus
Deborah Cameron (2007):
Underestimates differences within genders
Differences may reflect different social roles, rather than differences between men & women
E.g., tag questions
(1) Empirical Evidence
Mulac et al. (1998)
Participants rated transcribed conversations:
Men rated listener responses and questions as sig. more controlling
i.e., leading the conversation
Women rated listener responses as sig. more other-focused
i.e., showing interest
Men rated questions as sig. more sensitive
Thus, men & women interpreted language in different ways
Women in Politics
Female Suffrage
1893
First country to give women the vote was New Zealand.
1918
Women in UK given right to vote (only householders aged 30+)
Only 6 million women met this criteria at the time.
1920
USA (21+)
1928
UK (21+)
Political Representation
1919
First woman MP in UK House of Commons
1979
First UK female Prime Minister (Margaret Thatcher, followed by Theresa May in 2016)
2018
20 women are Heads of State/Government (6.3% of all international leaders)
2019
~33.8% (220/650) UK MPs are female (highest ever)
"He Runs She Runs"
Award-winning book
Brooks (2013)
: To what extent are gender stereotypes applied to political candidates?
Data collected 17-19 April 2009 (USA)
Online survey:
Corresponded to demographic characteristics of USA in gender, ethnicity, college education & age.
Designed to be representative.
Experimental Design...
Respondents read newspaper article about a fictional political candidate:
2 versions – only gender is varied (Karen Bailey or Kevin Bailey)
Participants were unaware that gender was a changing factor, only exposed to one version.
Random assignment to condition
Respond to a series of questions about the candidate
Three main dependent variables:
(1) Overall favourability
(2) Likely effectiveness in the Senate
(3) Likely effectiveness as US president in about 10 years.
Results:
(1) Few sig. gender effects:
Experience in office:
No effect
Emotional displays & knowledge gaffes:
Worse ratings, but no effects for gender
(2) Results potentially support women candidates seeking political office
UK General Election 2015
Cameron & Shaw (2016):
7 political leaders (3 female)
Men spoke more than women – but may reflect differences in party status
Assertion:
Interruptions from all speakers
Most aggravated examples from females
Andalusia
Regional parliament of Andalusia in Spain:
Men and women must have equal representation by law
Must be equally represented at all levels of the parliamentary political hierarchy.
Fuentes-Rodriguez & Álvarez-Benito (2016):
Men & women use similar strategies of persuasion and argument
Differences may reflect differences in party roles
e.g., whether in government or in opposition, not differences in gender