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Purpose: This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications. Design/methodology/approach: Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations. Findings: This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers. Originality/value: This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

During South Africa's apartheid era, the social and political ideology of Afrikaner nationalism Othered those who disrupted an idealised vision of white heteronormative Afrikaner masculinity. While protections from discrimination on the basis of sexual orientation were enshrined in the post-apartheid constitution in 1996, anti-LGBTQ violence remains a reality. It is common for young LGBTQ individuals to exercise the personal freedoms associated with leaving home to attend university to express their sexual orientations and/or gender identities more readily. This paper applies interpretive phenomenology to interview data with members of the LGBTQ student community at a rural university town in an overwhelmingly white Afrikaans region. It explores the complex and ill-defined relationships between LGBTQ student experiences, local heteronormative values, and shifting institutional identities in an era of social transformation.