职位头衔之下的脉搏：来自7.5亿条中国招聘广告的月度任职要求与任务读数
The Pulse Beneath the Job Title: Monthly Readings of Requirements and Tasks from 750 Million Chinese Job Ads
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中文总结 AI 辅助
本研究从7.526亿条中国招聘广告中构建任职要求与任务目录，区分二者差异，揭示了会计职业的薪资阶梯及受语言模型影响的工作的真实变化情况。
中文摘要 AI 辅助
如何定义一种职业？依据其职位头衔吗？一家小型贸易公司的会计负责记账；而在上市公司，相同的头衔要求具备注册会计师执照，每周需完成监管机构和董事会要求的报告。相同的头衔，标准不同，工作内容也不同。定义一种职业的是其准入门槛和要求从业者完成的任务。在快速变化的劳动力市场中，追踪这些任职要求和任务是把握市场脉搏的方式。然而，目前尚无工具能以其变化的速度同时读取这两者。像O*NET这样的官方职业目录，每个职业仅报告一个全国平均值，且每几年更新一次。招聘广告虽及时但非结构化，基于招聘广告的研究多依赖职位头衔加上专有技能关键词，这将对求职者的要求与要求求职者做的事混为一谈。这种混淆很重要，因为任职要求上升和任务变化是不同的事件，有着不同的原因。我们将二者区分开来。我们从2022年至2026年中国五大招聘平台发布的7.526亿条招聘广告中，提取雇主撰写的短语，统一指代同一事物的短语，并验证从文本到条目的映射。通过这样做，我们构建了两个目录：20721项求职者必须满足的任职要求，以及44479项被录用者将执行的任务。在对条目进行标准化后，我们进一步对其进行标注。例如，每个任务都带有一个语言模型可吸收程度的评分。将这些目录匹配回每一条招聘广告，即可逐月读取市场情况。两个例子显示了职位头衔之下这一层次的价值：其一，职业登记处记录一种会计，而招聘广告记录的是一个阶梯——薪资范围底层的初级证书，以及顶层的中级证书；其二，统计职业数量显示最易受语言模型影响的工作正在消失，而统计任务数量显示其消失的程度要小得多。
英文摘要
How do we define an occupation? By its job title? An accountant at a small trading company keeps the books; at a listed firm the same title demands a certified-accountant licence, and the week goes to the reports that regulators and the board read. Same title, different bar, different work. What defines an occupation is who it lets in and what it asks them to do. In a rapidly changing labor market, tracking those requirements and tasks is how to take the market's pulse. Yet no instrument reads both at the speed they change. Official occupational directories like O*NET report one national average per occupation, updated every few years. Job postings are timely but unstructured. Research built on them works from job titles plus proprietary skill keywords, which blur what is asked of a candidate into what a candidate is asked to do. The blur matters, because rising requirements and changing tasks are different events with different causes. We separate them. From 752.6 million job ads posted on China's five leading recruitment platforms between 2022 and 2026, we extract the phrases employers write, unify those that name the same thing, and validate the mapping from text back to entry. By doing so we construct two catalogs, 20,721 requirements a candidate must meet and 44,479 tasks the hire will do. With the entries standardized, we annotate them further. Each task, for example, carries a score for how far a language model could absorb it. Matched back onto every ad, the catalogs read the market month by month. Two examples show what the layer beneath the job title buys. First, the occupational registry records one accountant where the ads record a staircase, the junior certificate at the bottom of the wage range and the intermediate one at the top. Second, counting occupations says the work most exposed to language models is disappearing, and counting tasks says far less of it is.
