Career mobility as a Poisson process: An application to the career dynamics of men and women in the U.S. Office of the Comptroller of the Currency from the Civil War to World War II

Poisson process models of upward mobility in job rewards are developed and estimated using event history techniques on data from one organization over a period of 80 years. The models developed permit the inclusion of independent variables which vary over a person's career and are used to analyze differences in the mobility of men and women. The main findings are (1) the career dynamics of men and women are similar in general form, both best described by a segmented, heterogeneous, and nonstationary Poisson process; (2) parametric differences exist between the models for men and women in terms of how certain independent variables affect the rate of arrival of opportunities to move up in the reward hierarchy and in terms of how others affect the rate of being given or taking advantage of such opportunities; and (3) both men and women appear to benefit by being in competition with one another for available opportunities. Theoretical interpretations of these results are discussed in relation to differences in the flow of opportunities to locations in the reward hierarchy occupied predominantly by men versus those occupied predominantly by women.     

生成式人工智能介入知识生产的功用探析——借助ChatGPT和“文心一言”探究数字劳动的体验

ChatGPT和“文心一言”作为生成式人工智能的新产品,造就了人机合作进行知识生产（尤其是学术研究）的新形式。以探究数字劳动的含义为例,以人机问答的方式获得机器智能提供的答案,这展现了ChatGPT和“文心一言”可以辅助人进行学术研究的新功用,从而形成人机合作的知识生产新模式。在这种模式中,机器智能在提供资源的概括和简单的文本生成方面对人的知识生产提供高效的帮助,并通过人机问答的互动使这种帮助更具针对性和时效性。通过对机器生成内容的具体考察,深入体验这两款生成式人工智能新产品的实际使用,也可以发现其明显的不足和局限,尤其体现在：由于受限于训练数据集和能够进入的学术资源数据库,其归纳既有知识成果的全面性和准确性与人的要求还有较大的差距,更不能替代人去进行原创性的知识创新。鉴于此,需要理性地看待智能机器为人类的知识生产所能起到的功用,既要前瞻性预测这一新型的人机协同方式对人类未来知识生产走向的巨大影响和积极意义,也要根据现阶段生成式人工智能的工作机制,发挥好人在使用提示词等方面的作用,通过人的积极作为来调动智能辅助系统的潜能,在人机之间既有合理的分工又有融洽的互补式合作,来助力学术研究型知识生产活动的开展,并对人作为知识生产的主体始终保持足够的警醒。