Ageist Stereotypes in Employment: Machine learning vs Qualitative Analysis of bias in rhetoric

The rhetoric which unions and employers use to discuss age and to defend their positions is relatively similar and unions use ageist stereotypes to defend early retirement and fight against raising pension ages. Based on the framework proposed by Collien, Sieben & Müller-Camen (2016) we are analysing the language that both employers and unions use to describe workers for ageist bias.

We have a large dataset based on interviews and responses to public consultations, using this data we are training a machine learning model to identify potentially ageist language.

Data cleaning, qualitative (discourse) analysis, we will also train you in the basics of word2vec deep learning.

We have two large datasets of employers and union officials discussion policies related to age management and older workers.  The first is a set of workshop discussions with the two groups, while the second is a set of written submissions to a national consultation on rising pension ages in the NHS.

The project will involve three phases:

• first the project team (including the RA) will be manually coding the data using a Discourse Theory framework to identify arguments which social partners make to either challenge or reinforce age stereotypes in order to champion or defend interests;
• second, we will use machine learning to conduct the same analysis.
• Third, we will compare the two results in order to identify the advantages and disadvantages of AI in coding in terms or rigour and biases.  This is a great opportunity for a PhD student in linguistics to work in an interdisciplinary team with academics in management and machine learning to investigate business rhetoric and biases within social dialogue.

Authorship of a British Journal of Management paper, potential attendance at a national conference to present our paper (or potentially the academy of management conference in Copenhagen in July 2025 which we have already been accepted to) and the opportunity to work with the Artificial Intelligence in Mental Health lab at King’s College London.

The student will also be invited to join networks which the co-I’s are involved including the COST-Action programme Digi-Net (www.digineteu.eu) which will provide opportunities for networking with a multi-national and multidisciplinary network of academics as well as training and membership in its Young Researcher Network. The project team also has a good track record in disseminating research to the broader public (including good practice guides, videos, and public speaking events) which will enhance the student’s profile and impact.

• Experience in qualitative data analysis, ideally discourse analysis.
• Eagerness to learn.

Conference presentations, academic papers

1. Please send your CV and a brief cover letter outlining your interest and suitability to the project supervisor(s). Please contact the project supervisor(s) in advance of submitting the application with any questions.

2. If selected by the project supervisor

• LISS DTP students must then complete the LISS DTP Placement /Internship Application form. This ensures that there is approval of PhD supervisor, and the necessary information is obtained to extend funding (for DTP1 students) or confirm placement requirement fulfilled (for DTP2 students), and to fulfil ESRC reporting obligations. LISS DTP approval must be given before the RA internship can commence.
• Other ESRC-funded DTP students should follow the internship application processes from their home DTP.

Please note for LISS DTP students:

• Research Assistant Internships must not be undertaken with the student’s current supervisor and/or home department.
• DTP1 students (those whose funding commenced before Oct24): a maximum of 4 Research Assistant internships will be funded. These will be filled on a first-come, first-served basis. Once the 4 DTP1 places are filled, we will inform PIs that only DTP2 students are eligible for the Research Assistant internships. PLEASE NOTE THAT ALL DTP1 PLACES HAVE NOW BEEN FILLED.
• DTP2 students (those whose funding commenced from Oct24): are required to complete a 3-month placement, which is funded through their studentship. No limits to number that can be funded.
• Reports: at the conclusion of the internship, the student will be required to complete an internship report, which will include a question for the internship host to feedback on the internship.

Contact liss-dtp@kcl.ac.uk with any questions.