执行与评估：一种新的职业衡量标准和长期就业梯度
Execution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients
- Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究通过对O*NET任务陈述评分构建职业执行和AI能力占比，发现执行占比有特点，且执行繁重的白领职业就业增长低，2022年后能力梯度变陡，确立了衡量标准和时间顺序，未证明AI因果影响。
AI中文摘要：
人工智能在执行方面比评估更容易实现自动化：生成输出成本低，判断其是否正确成本高。现有衡量标准根据人工智能能否执行任务对工作进行排名，而非人类所提供的功能。作者对19265条O*NET任务陈述进行评分，构建职业层面的执行和人工智能能力占比。执行占比在不同模型编码者和O*NET版本间具有可重复性，与人工智能能力和常规任务强度不同。在一个协调面板中，自2012年以来，执行繁重的白领职业在各时间段就业增长较低，且斜率平等不能被拒绝。2022年后，有效能力梯度变陡，但无法确定因果关系。证据确立了一种衡量标准和时间顺序，而非人工智能导致的影响。
英文摘要：
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.
