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"Learn to blend in!": A corpus-based analysis of the representation of women in mining
Luleå University of Technology, Department of Arts, Communication and Education, Education, Language, and Teaching.ORCID iD: 0000-0001-9170-1459
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Human and technology.ORCID iD: 0000-0002-6330-2992
2018 (English)In: Equality, Diversity and Inclusion, ISSN 2040-7149, E-ISSN 2040-7157Article in journal (Refereed) Epub ahead of print
Abstract [en]

Purpose

The aim of this study is to contribute with increased knowledge about gender in mining by exploring how women are discursively represented in texts produced by actors in the international mining arena.

Design/methodology/approach

The study combines corpus linguistic methods and discourse analysis. It implies a combination of quantitative and qualitative analyses, where the former is used as the point of departure for the latter, and where the material analysed is chosen on the basis of certain selected search phrases. The source for the study is the web, and the search engine used for the retrieval of data is WebCorp Live, a tool tailored for linguistic analysis of web material.

Findings

The analysis reveals that although the overarching theme in the women-in mining discourse is that women are needed in the industry, the underlying message is that women in mining are perceived as problematic.

Practical implications

The study shows that if mining is to change into a modern industry, the inherent hyper-masculine culture and its effects on the whole industry needs to be problematised and made evident. To increase the mere number of women, with women still heavily underrepresented, is not enough to break gender-biased discrimination.

Originality/value

The research contributes with new knowledge about gender in mining by using a method, which so far has had limited usage in (critical) discourse analysis.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2018.
National Category
Didactics Production Engineering, Human Work Science and Ergonomics
Research subject
English and Education; Human Work Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-70215DOI: 10.1108/EDI-12-2017-0270OAI: oai:DiVA.org:ltu-70215DiVA, id: diva2:1236875
Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-08-13

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Norberg, Cathrine

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