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  "documentTitle": "Effects of GenAI on the German labor market",
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  "notes": "The methodology relies on mapping O*NET task descriptions to AI patent data to calculate an impact score.",
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      "text": "Research shows most US jobs could have moderate to high exposure to AI, with high or very high augmentation for roughly a third of those.",
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      "text": "For example, the role of an agricultural technician includes a task for 'developing soil sampling grids,' which has an associated verb-noun pair of 'develop grid' representing 5% of an agricultural technicians' functions.",
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      "text": "The verb-noun pairs are then compared to patents filed for AI technology... to see how exposed they are to AI. The sum product of each task's exposure score and the frequency of tasks in every occupation is then used to estimate an aggregate raw AI impact score per occupation.",
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      "text": "To estimate the potential impact of GenAI across occupations, we leveraged research from Michael Webb at Stanford. The analysis uses a verb-noun pairing framework covering over 800 occupations and their task descriptions from O*NET.",
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      "text": "Webb, Michael, \"The Impact of Artificial Intelligence on the Labor Market,\" SSNR - Elsevier, 11 January 2020",
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      "text": "References: 1. Webb, Michael, 'The Impact of Artificial Intelligence on the Labor Market,' SSNR - Elsevier, 11 January 2020",
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