umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Raising Language Awareness Using Digital Media:Methods for Revealing Linguistic Stereotyping
Umeå University, Faculty of Arts, Department of language studies.
Umeå University, Faculty of Arts, Department of language studies.
2016 (English)In: Reserach Methods for Creating and Curating Data in the Digital Humanities / [ed] Matt Hayler and Gabrielle Griffin, Edinburgh: Edinburgh University Press, 2016, 158-180 p.Chapter in book (Refereed)
Abstract [en]

Whether we are aware of it or not, language is at the heart of the mechanisms leading to stereotyping and inequality. It is one of the major factors that we evaluate when we meet others, and it has long been demonstrated that individuals are judged in terms of intellect and other character traits on the basis of their language output (e.g. Cavallaro & Chin 2009). We also adapt our own language to fit underlying norms and preconceived social stereotypes when we communicate with others. In this way, we help to shape individuals through the way we treat them linguistically, and social identity expressed through language is consequently something that is renegotiated during every meeting between humans (Crawford 1995). An awareness of such mechanisms is especially important for teachers.

In most language courses aimed at student teachers of various levels, students are given a theoretical overview of research on aspects related to identity (gender, ethnicity, social class etc.) and language. But however well intended, there is a real danger that research focussed on identifying differences also strengthens stereotypes. Further, there is a risk that such theoretical knowledge remains just that; creating the link between so-called factual knowledge – for example, theoretical frameworks and previous studies – and internalized knowledge, applicable in our everyday lives, is especially challenging. This is particularly true in the domain of language, where metalinguistic knowledge ideally should be translated into professional language practice, a key skill for anyone working with human interaction.

The Chapter explores  preliminary experiments conducted in 2011 where we were able to use digital media in order manipulate identity variables such as gender, and describes the aim of the current project - to further develop and explore experiential pedagogic approaches aimed at raising sociolinguistic language awareness about conceived identity-related phenomena in language, and to systematically test the effects of these methods. The project thereby combines the fields of sociolinguistics, social psychology and digital humanities in an innovative way with the objective to produce tested methods for exposing and combatting linguistic stereotyping. 

Place, publisher, year, edition, pages
Edinburgh: Edinburgh University Press, 2016. 158-180 p.
Keyword [en]
Stereotyping, matched-guise, gender, language
National Category
Specific Languages
Research subject
Linguistics
Identifiers
URN: urn:nbn:se:umu:diva-128313ISBN: 9781474409650 (print)OAI: oai:DiVA.org:umu-128313DiVA: diva2:1051325
Projects
RAVEC-RAVE
Funder
Swedish Research Council, 2014-1972Marcus and Amalia Wallenberg Foundation, MAW 2103.0103
Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2016-12-01

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Deutschmann, Mats
By organisation
Department of language studies
Specific Languages

Search outside of DiVA

GoogleGoogle Scholar

Total: 72 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf