Beyond the gender bias: Identifying gendered language in job advertisements and predicting organisational diversity outcomes using embedding methods, POS tagging and predictive modelling

Additional Metadata
Keywords	gender inequality; diversity; gendered wording; job advertisements; NLP
Thesis Advisor	Malek, S.L.
Persistent URL	hdl.handle.net/2105/59117
Series	Business Economics
Organisation	Erasmus School of Economics
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Giesen, K.A. (2021, October). Beyond the gender bias: Identifying gendered language in job advertisements and predicting organisational diversity outcomes using embedding methods, POS tagging and predictive modelling. Business Economics. Retrieved from http://hdl.handle.net/2105/59117

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