This project investigates the susceptibility of jobs to computerisation and particularly which features of a job determine the probability of computerisation. This is achieved by using Gaussian Process Classification. A set of labelled occupations is used to train and test the model and the effect of using different feature sets is explored. Feature selection in the form of greedy selection is used to find the feature set that achieves the best classification and thereby find the features that are most significant when determining if a job can be computerised. It is concluded that the most important feature is Originality and the best feature set for classifying the data in this project consists of Originality and Service Orientation. Furthermore, experiments are performed using linear embedding methods for feature learning. However, these experiments fail to prove that better classification can be achieved using this method.
Other Recent Journal Article / Working Papers
Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil
SARS-CoV-2 elimination, not mitigation, creates best outcomes for health, the economy, and civil liberties
Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis
Demand-pull and technology-push: What drives the direction of technological change? An empirical network-based approach
Productivity Dispersion, Wage Dispersion and Superstar Firms
Future Series: Cybersecurity, emerging technology and systemic risk