English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Using high-quality nation-wide social security data combined with machinelearning tools, we develop predictive models of income support receiptintensities for any payment enrolee in the Australian social security systembetween 2014 and 2018. We show that off-the-shelf machine learning algorithmscan significantly improve predictive accuracy compared to simpler heuristicmodels or early warning systems currently in use. Specifically, the formerpredicts the proportion of time individuals are on income support in thesubsequent four years with greater accuracy, by a magnitude of at least 22% (14percentage points increase in the R2), compared to the latter. This gain can beachieved at no extra cost to practitioners since the algorithms useadministrative data currently available to caseworkers. Consequently, ourmachine learning algorithms can improve the detection of long-term incomesupport recipients, which can potentially provide governments with largesavings in accrued welfare costs.

Using Machine Learning to Create an Early Warning System for Welfare Recipients