一种模式不适合所有:利用在线劳动力市场数据构建互补的数字再技能战略

F. Stephany
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引用次数: 0

摘要

数字技术正在从根本上改变我们的工作环境和对技能的需求,某些工作正在被自动化取代,而另一些工作则需要掌握新的数字技术。由于任务自动化导致的技能需求快速变化的全球挑战使工人不堪重负。数字技能差距进一步扩大,因为技术和社会转型的速度超过了国家教育体系,而掌握人工智能(ai)等新兴技术的精确技能要求仍然不透明。在线劳动力平台可以帮助我们理解大规模再培训的巨大挑战。在线劳动力平台建立了一个全球一体化的市场,在数百万远程交付的认知工作的买家和卖家之间进行调解。这篇评论认为,在过去十年中,在线劳动力平台已成为技能重新捆绑的“实验室”;来自不同职业领域的技能组合。在线劳动力平台数据使我们能够建立一个关于个人技能互补性的新分类。对于政策制定者、教育提供者和招聘人员来说,对互补的再培训轨迹进行持续分析,可以自动、个性化和有远见地建议在技术颠覆的未来学习新技能的价值。
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One Size Does not Fit All: Constructing Complementary Digital Re-Skilling Strategies Using Online Labour Market Data
Digital technologies are radically transforming our work environments and demand for skills, with certain jobs being automated away and others demanding mastery of new digital techniques. This global challenge of rapidly changing skill requirements due to task automation overwhelms workers. The digital skill gap widens further as technological and social transformation outpaces national education systems and precise skill requirements for mastering emerging technologies, such as Artificial Intelligence, remain opaque. Online labour platforms could help us to understand this grand challenge of reskilling en masse. Online labour platforms build a globally integrated market that mediates between millions of buyers and sellers of remotely deliverable cognitive work. This commentary argues that, over the last decade, online labour platforms have become the ‘laboratories’ of skill rebundling; the combination of skills from different occupational domains. Online labour platform data allows us to establish a new taxonomy on the individual complementarity of skills. For policy makers, education providers and recruiters, a continuous analysis of complementary reskilling trajectories enables automated, individual and far-sighted suggestions on the value of learning a new skill in a future of technological disruption.
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