Presented by Rosie Oxbury and Liz Gallaher, Nesta
In this webinar, we will introduce a new tool we have created for extracting insights into “offered job quality” from job adverts. The tool is underpinned by a taxonomy based on the RSA’s Measuring Good Work report, enhanced with additional insights from Sleeman (2024). By identifying relevant sections within job adverts and mapping them to this taxonomy, the package provides a way to automatically extract indicators of job quality from job adverts. We will explain how we have applied the tool to two datasets from the Open Jobs Observatory, uncovering insights into job quality in England overall, and within the Early Years Practitioner sector – a field currently at risk of labour shortages. The package is freely available on Nesta’s GitHub, and we invite researchers to explore its capabilities and share their feedback.
Rosie Oxbury is a Data Scientist at Nesta. She specialises in supervised and unsupervised natural language processing techniques. Her work has included using job adverts to extract information about job quality.
Liz Gallagher is a Data Scientist at Nesta. She is interested in using natural language processing techniques to extract insights from data. Her work has included using job adverts to build a skills taxonomy and extract skills.
Discussant: Julia Schmidt, OECD