arXiv cs.CY· Li Gan·· 9 天前AI 评分24
研究提出职业"执行与评估"新度量：执行密集型白领岗位就业增长持续走低
Execution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients
AI 导读
一项研究对 O*NET 全部 19,265 条任务陈述按固定评分标准打分，构建职业层面的"执行"与 AI 能力占比指标，该执行占比在不同模型编码器和 O*NET 版本间可复现，且区别于 AI 能力和常规任务强度。
正文 · 原文
Abstract:Artificial intelligence automates execution more readily than evaluation: producing output is cheap, judging whether it is correct is not. Exposure measures rank tasks by whether AI can perform them, not by which function the human supplies. I score all $19{,}265$ O*NET task statements under fixed rubrics to build occupation-level execution and AI-capability shares. The execution share is reproducible across model coders and O*NET vintages and distinct from AI capability and routine-task intensity; it is a model-based measure, not human-validated ground truth, and adds only modest power beyond O*NET's evaluation activities. In a harmonized panel, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and equality of slopes cannot be rejected: the gradient is a secular trend rather than an AI-era event, largely between occupational families. The vintage-valid capability gradient steepens after 2022, a change that is dated but not causally attributable. The evidence establishes a measure and a chronology, not an AI-caused effect.
| Subjects: | General Economics (econ.GN); Computers and Society (cs.CY) |
| Cite as: | arXiv:2607.20807 [econ.GN] |
| (or arXiv:2607.20807v1 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2607.20807 arXiv-issued DOI via DataCite |
Submission history
From: Li Gan [view email]
[v1]
Thu, 23 Jul 2026 00:29:17 UTC (771 KB)
来源：arXiv cs.CY · arxiv.org