A lot of sociolinguistic work has focussed on how males and females use linguistic features in spoken language. This research has led to certain features being associated more with male use, such as I references (e.g. I think….) and quantity references (e.g. it was 24 metres), while references to emotion (e.g. a happy occasion) and verbs expressing uncertainty (e.g. it seems to be…) are linked to female use.
But are these gender-associated language features also used in written language? Anthony Mulac, Howard Giles, James J. Bradac and Nicholas A. Palomares enlisted the help of 127 19-21 year old students and asked them to produce a written description of 5 different photographic images depicting nature scenes (such as a mountain reflected in a lake). The experiment had five stages. Participants had to:
- · write a description of the first image (this was the control task - as no other instruction was given, the researchers assumed that this was a natural reflection of the participants’ language use)
- · write a description as if they were:
a. a man describing it to a man
b. aman describing it to a woman
c. a woman describing it to a man
d. a woman describing it to a woman
By imposing these conditions, Mulac and his colleagues could test whether the writers’ language altered according to the gender of the perceived recipient of the description or the gender persona that they were told to adopt.
Each description from the participants was anonymously coded for gender-specific language features (such as those mentioned above) and the control description was used as a base for comparing their language use in the other four scenarios.
The results showed gender differentiation in the the control task. In the natural descriptions (with no instruction from the researchers), males and females used more of the features associated with their gender. The researchers note that this is evidence of gender-linked language at an unconscious level.
In addition, when the participants were asked to write as either a male or female, there was an increase in their use of appropriate gender features. For example, males writing under the guise of a female adopted more ‘female’ features, such as emotional references, while females writing under a male guise increased the use of ‘male’ features like quantity references. This, the researchers suggest, means that, in addition to unconscious knowledge of gender-linked language, there are some features of language that are gender-linked stereotypes. These stereotyped features can be accessed and manipulated by people when they want to present different gender affiliations.
In contrast, the results did not show any manipulation of gender features according to the perceived audience (for example, males didn’t alter their language use according to whether they were writing to a female or male). Also, the results didn’t show any increase in gender features when writing to someone of the same gender. Previous research had suggested that, for example, a male conversing with another male may increase his use of ‘male language features’ in order to promote his sense of maleness. Instead, Mulac, Giles, Bradac and Palomares suggest that, as respondents used a combination of features when writing to other people, they were styling their speech so that it did not heavily emphasise one gender or another. They were, in a sense androgynous.
In conclusion, therefore, the researchers propose that individuals have gender schemata and stereotypes. The former generate gendered language features in an unconscious sense (hence the control descriptions show many gender-associated features). The latter allows us to consciously draw on our knowledge of gendered language when we are prompted to do so. It is interesting that both the schemata and the stereotypes produce similar linguistic features, as the features used by participants (be it consciously or unconsciously) were consistent across the tasks.
Mulac, Anthony, Giles, Howard, Bradac, James J. and Palomares, Nicholas A. (2013) The gender-linked language effect: an empirical test of a general process model. Language Sciences 38: 22-31
This summary was written by Jenny Amos