Completed and submitted to the Australian Government in May 2020, this report was made publicly available in March 2023. The review analysed older report reports that highlighted deficiencies with current classification arrangements and makes recommendations for change informed by the need for a future classification regulatory framework.

While DASSH supports the principles outlined in the Discussion Paper in general, members suggested that the task of research classification could be more effectively achieved and efficiently undertaken were it to draw on techniques arising from advances in artificial intelligence, machine learning, and data science.

In this work we offer an open and data-driven skills taxonomy, which is independent of ESCO and O*NET, two popular available taxonomies that are expert-derived. Since the taxonomy is created in an algorithmic way without expert elicitation, it can be quickly updated to reflect changes in labour demand and provide timely insights to support labour...

Bibliometric databases on publications and their citations offer the possibility to quantify the scale and impact of this key output of scientific research. But there are many decisions to be made about how to construct these bibliometric measures, and no global consensus on how best to do so. We consider several measures of research output...

Misclassification of employees as independent contractors exacts an enormous toll on workers, law-abiding employers, and our economy. Accurate information on the prevalence of the problem, and on patterns of violations, can help government officials to direct their efforts at the worst violators and most problematic industries.