The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
OECD Publishing, Paris
2 Pith papers cite this work, alongside 424 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · OpenAlex
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2026 2representative citing papers
Proposes GAGI, a publicly computable index adjusting GDP per capita for inequality and prices to monitor welfare-adjusted prosperity in G7 economies from 2010-2026.
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Jobs' AI Exposure Should Be Measured from Evidence, Not Model Priors
The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
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GAGI: A Gini-Adjusted GDP-per-Capita Index for Distribution-Aware Macroeconomic Welfare Monitoring
Proposes GAGI, a publicly computable index adjusting GDP per capita for inequality and prices to monitor welfare-adjusted prosperity in G7 economies from 2010-2026.