Another peak body says research codes don’t currently cut it – as for the future, try AI.

The research classification review (CMM March 25) should drop the six-digit specialist-discipline category, the Deans of Arts, Social Sciences and Humanities recommend in a submission to administering ANZ agencies;

“classification at the six-digit fields can at times be arbitrary (e.g. where no six-digit field suitably describes the activity, researchers or administrators are forced to select from a range of equally unsuitable codes), or unnecessarily complex (e.g. where multiple six-digit fields are appropriate within the same four-digit group),” DASH states.

The allocation of six-digit codes also “submerges” research streams and is, “no way reflective of the current state or directions of literary or historical studies.”  DASH points to “postcolonial” and “transnational” research as areas that are lost. Pedantically precise codes also create nonsensical classifications, “such as 200501 (Aboriginal and Torres Strait Islander Literature) and 200502 (Australian Literature excluding ATSI Literature)”.  To ensure ATSI and related research does not disappear in the coding confusion, DASH suggests, “an optional tick-box or supplementary code level to indicate that the research activity relates to Aboriginal and Torres Strait Islander, Māori, and/or Pacific Peoples.”

As to identifying inter-disciplinary research; “codes could be introduced as a two-digit division to capture inter/multidisciplinary research, however some members considered revising entirely the basis for the taxonomy and working with data scientists to design new classification methods to be a better option.”
In the future it may not people doing the designing. While such “may be overly ambitious now,” “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.”  DASH accordingly recommends regulators “refine and improve the classification system through the current exercise with a view to developing an automated classification system in future.”