【作者简介】张顺,西安交通大学人文社会科学学院社会学系;吕风光,西安交通大学人文社会科学学院社会学系

【文章来源】《社会》2024年第5期

【内容提要】本文从风险视角切入，揭示了在人工智能时代的背景下职业风险影响主观阶层认同的作用机制，厘清了阶层认同风险逻辑与资源逻辑的区别与联系，超越了以往基于资源逻辑的“阶层认同形成理论”。研究发现，智能时代的职业风险具有显著的阶层认同下移效应，失业风险和收入风险是职业风险影响阶层认同的双重传导机制。资源逻辑对职业风险的阶层认同下移效应具有调节效应，拥有更多资产的劳动者，职业风险对阶层认同的降低作用相对较弱；相对而言，中等收入群体面临的职业风险更高，从而使其阶层认同下移程度更大。本文从风险视角解释了主观阶层认同的下偏现象，同时较好地回应了中等收入群体“低位认同”的“时代之力”，对理解数字经济下社会分层的变化趋势有启发意义。

【关 键 词】智能时代, 职业风险, 阶层认同, 风险视角, 中等收入群体

【基金项目】本研究得到国家社会科学基金重大项目“新形势下我国面临的主要就业风险及多维治理研究”（21&ZD181）的资助。

【全文链接】https://www.society.shu.edu.cn/CN/Y2024/V44/I5/208

The Impact of Occupational Risks on Subjective Class Identification in the AI Age

Abstract: Accompanying the rapid economic development and reforms in income distribution， Chinese residents have enjoyed a continuous growth in earnings， and the middle-income group has expanded. However， subjective class identification continues to be characterized by a downward bias， with the phenomenon of downward identification among the middle-income group being particularly pronounced. The gap between objective and subjective status remains a mystery. In parallel with the expansion of the middle-income group, the rapid development of artificial intelligence technology in recent years has triggered a change in productivity that has propelled us into the AI age. As AI progresses， the work tasks of many occupations are at risk of being replaced by the technology， which in turn has impacted the social mindset of the population. This paper adopts a risk perspective to reveal the mechanisms through which occupational risks， in the context of AI， affect subjective class identification. Our research finds that occupational risks have a significant downshift effect on class identification. Further analysis indicates that unemployment and income risk are two dual transmission mechanisms through which occupational risks influence subjective class identification. In addition， personal assets moderate the effect of occupational risks on the downward class identification. Workers with more recourses are less impacted by occupational risks on their class identification. The analysis of the middle- income group shows that， compared to low-income and high -income groups， the group faces higher occupational risks， resulting in a greater degree of downshift bias in class identification. This study explains the downward bias in subjective class identification from a risk perspective， offering a significant contribution to the traditional resource-based theories of class identification. Furthermore， our research addresses the question of the “power of era” that causes the lower subjective class identification of middle-income groups， providing important insights into understanding the trends in social stratification in the AI age.

Key words: AI age, occupational risks, subjective class identification, risk perspective, middle-income group