Gender & Communication Lecture Notes PDF

Gender & Communication  Plan of Lecture 1. Nonverbal communication: 1.1 Judgement accuracy How well one can judge others 1.2 Expression accuracy How well one can communicate to others 1.3 Channel differences Forms of NVC (e.g., gaze, s...

Gender & Communication  Plan of Lecture 1. Nonverbal communication: 1.1 Judgement accuracy How well one can judge others 1.2 Expression accuracy How well one can communicate to others 1.3 Channel differences Forms of NVC (e.g., gaze, smiling) 2. Speech: 2.1 Gender, language & power Gender differences in language related to societal power? 2.2 The two-cultures approach Alternative view: “2 cultures”, affects communication 3. Women in politics Some recent data  1.1 NVC: Judgement Accuracy Hall (1978) – Review of 75 studies (posed and spontaneous expressions): posed – i.e., Ps asked to adopt expression spontaneous – e.g., Ps watch video which evokes a certain emotion. Decoder task: identify nature of film  1.1 NVC: Judgement Accuracy Hall (1978) – Review of 75 studies (posed and spontaneous expressions): 24 significant difference 23 in favour of women Hall (1984) reviewed a further 50 studies: 11 significant difference 10 in favour of women  1.2. Expression Accuracy 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 Many indicate women typically encode more clearly (i.e., their NVB is easier to read) N.B. Accuracy does not necessarily represent a social advantage – Importance of situational context  1.3 Channel Differences (a) 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- to 23-year- old group (>13-17 > 24-64) Some cultural variation in these gender differences  1.3 Channel Differences (b) 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 1. Social power 2. 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  2. Gender Differences in Speech 2.1 Gender, language & power 2.2 The two-cultures approach 2.1 Gender, Language & Power Lakoff (1973): “Women’s language” A language of “powerlessness” The deficit model – Male way of speaking is normative – Female way deviates from norm 2.1 Gender, Language & Power Lakoff (1973): Female speech Forms of politeness Tactful, hesitant, lower in authority Hedges (mitigating devices which lessen the impact of an utterance, e.g., perhaps, sometimes, I think, kind of) Tag questions (e.g., doesn’t it?, isn’t it?) Higher in grammatical accuracy Intensifiers (e.g., extremely, so, very) Direct quotes Low in humour 2.1 Gender, Language & Power Lakoff (1973): Male speech More direct, explicit (e.g., “Give me that”) More interruptions (for control of conversation) More foul language More simplified language (e.g., dumbing down for social bonding) Higher in humour 2.1 Gender, Language & Power Many of Lakoff’s claims based on personal obs. Two features to be discussed from empirical research: ‒ Hedges (mitigating devices which lessen the impact of an utterance) ‒ Tag questions, e.g., doesn’t it?, isn’t it? (may turn a statement into a Q) Research by Holmes (1985, 1986)  Hedges – “You know” Holmes (1986) – Study of functions of “you know” Corpus of 50,000 words (25K per gender) ~20K from formal interactions (TV/radio interviews) ~30K informal (mealtime chats in private homes) N = 32F, 32M >200 instances of “y’know”  Hedges – “You know” Holmes (1986) – Study of functions of “you know” 2 different meanings: 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 N.B. Exactly the opposite of what Lakoff would expect  Hedges – “I think” Holmes (1985) – Analysis of the functions of the term “I think” Different meanings dependent on context: 1. Deliberative form (booster) 2. Tentative form (hedge) 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 (according to Lakoff) 2. Facilitate conversation 3. Confrontational 4. Soften the force of a criticism Uncertainty tags – more by men Facilitative tags – more by women The opposite of what Lakoff would expect  “Women’s Language”: Summary 1 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  “Women’s Language”: Summary 2 In defence of Lakoff: Link between language use, gender & power (Cameron, 2007) Stimulated much research on gender & language  “Powerful Language”: Interruptions Zimmerman & West (1975) – Opposite-sex conversations: Men typically interrupt Murray & Covelli (1988): Women interrupted men twice as often Anderson & Leaper (1998) – Meta-analysis: All interruptions (d=.15) [men>women] Intrusive interruptions (d=.33) But findings heavily qualified by situational and contextual factors 2.2 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 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 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  Evaluation of the Two-Cultures Approach 2. Polarisation E.g., popular best-seller Men are from Mars, Women are from Venus (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  Evaluation of the Two-Cultures Approach 3. The Myth of Mars and Venus Cameron (2007): – Underestimates differences within genders – Differences may reflect different social roles, rather than differences between men & women E.g., tag questions  Tag Questions Cameron et al. (1988): Assessing people in various jobs and activities Use of tag questions predicted better by social role than by gender: – Facilitative tags used by professionals (e.g., TV presenters, medics, teachers) To encourage interaction – Information-checking tags used by audience members, pupils & callers  3. Women in Power 3.1 Female Suffrage 1918 Women in UK given right to vote (only householders aged 30+) 6 million 1920 USA (21+) 1928 UK (21+)  3.2 Political Representation 1919 First woman MP in UK House of Commons 1979 First UK female Prime Minister 2024 ~40.5% (263/650) UK MPs are female 2024 30 women are elected Heads of State and/or Government (15% of the 193 UN member states) At current rate, gender parity in highest positions not until 2154  3.3 Gender Stereotypes He Runs, She Runs Brooks (2013): To what extent are gender stereotypes applied to political candidates? Data collected 17-19 April 2009 Online survey: – Corresponded to demographic characteristics of USA in gender, ethnicity, college education & age  He Runs, She Runs Experimental Design Respondents read newspaper article about a fictional political candidate: 2 versions – only gender is varied (Karen Bailey or Kevin Bailey) Random assignment to condition Respond to a series of questions about the candidate  He Runs, She Runs Experimental Design (cont.) Three main dependent variables: 1. Overall favourability 2. Likely effectiveness in the Senate 3. Likely effectiveness as US president in about 10 years  He Runs, She Runs 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  Gender Stereotypes Potential consequences Courtemanche & Connor Green (2020): General voter preference for women Political scandals: – Greater consequences for female politicians for alleged wrongdoing “Backlash” for gender norm violation?  3.4 Interactions UK General Election 2015 Cameron & Shaw (2016): Analysis of 2 televised debates: 1. All 7 main party leaders (Cameron, Clegg, Miliband, Sturgeon, Wood, Bennett, Farage) 4 Males, 3 Females 2. Opposition leaders only (Miliband, Sturgeon, Wood, Bennett, Farage) 3 Females, 2 males  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 (interruptions that more overtly violated an opponent’s turn) from females Comparable findings (Och, 2020) – “manterrupting” in the Bundestag [Lecture 1] Conclusion: no notable differences in linguistic behaviour related to gender  Interactions 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 For research, a rare opportunity to examine politicians’ interactions where the numbers are equal  Andalusia 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 Gender not significant in explaining language differences  Conclusions 1 Early research: sig. gender differences in both speech & nonverbal communication Two main forms of explanation: – Power – Style Two interpretations not necessarily mutually exclusive – To stress “cultural” difference is not to deny that dominance also exists  Conclusions 2 Third approach developed by Cameron (2007): Gender differences exaggerated Differences may reflect social roles rather than intrinsic differences between men & women Supported by recent research in politics Politicians talk like politicians – irrespective of gender Fuentes-Rodriguez & Álvarez-Benito (2016) reject Lakoff’s notion of “women’s language”.  Conclusions 3 Public typically does not have different standards for female candidates (Brooks, 2013) – Results potentially encouraging for women in running for office For allegations of wrongdoing, female politicians viewed more harshly (Courtemanche & Connor Green, 2020)